13.3.2 General Structure and Graph Representation, Relations, Neighbors

Chapter Contents (Back)
Object Recognition. Constraint Satisfaction. Matching, Graphs.
See also Spatial Information and Features, Visual Relationships. A lot of overlap with:
See also Matching Graphs and 3-D Network Descriptions. and Matching:
See also Structural Matching for Computer Vision.
See also Knowledge Distillation.

Winston, P.H.,
Learning Structural Descriptions from Examples,
PsychCV75(157-209). Chapter 5. BibRef 7500
And: Ph.D.Thesis (EE), BibRef MAC-TR-76, September, 1970. BibRef
And: MIT AI-TR-231, 1970.
WWW Link. Learning. Matching network descriptions. Winston built on the work of Guzman(
See also Computer Recognition of Three-Dimensional Objects in a Visual Scene. ) by using recognized blocks-world objects in a learning system. Three-dimensional structures are represented using semantic networks with elementary objects as a node and relations or descriptions given by the arcs. Object descriptions are learned by giving the system well selected examples that cause specializations or generalizations of the description. This work avoids the very real problem of extracting these descriptions from images, but provides a good introduction to the issues of high level computer vision. BibRef

Winston, P.H.,
Scene Understanding Systems,
FPR72(569-574), 1972. BibRef 7200

Winston, P.H.,
Learning and Reasoning by Analogy,
CACM(23), No. 12, December 1980, pp. 689-703. BibRef 8012
Earlier: MIT AI Memo-520, April 1979. BibRef

Winston, P.H.,
Learning New Principles from Precedents and Exercises,
AI(19), No. 3, November 1982, pp. 321-350.
Elsevier DOI Continuing learning, less on vision. BibRef 8211

Winston, P.H., Binford, T.O., Katz, B., and Lowry, M.,
Learning Physical Descriptions from Functional Definitions, Examples, and Precedents,
RR-IS84(xx). BibRef 8400
Earlier:
Learning Physical Descriptions from Functional Descriptions,
AAAI-83(433-439). BibRef

Evans, T.G.,
A Heuristic Program to Solve Geometry-Analogy Problems,
SJCC1964, AFIPS, Vol. 25, pp. 5-16. BibRef 6400
And: RCV87(444-455). Analogy. Graph descriptions of 2-D pictures. BibRef

Pavlidis, T.,
Representation of Figures by Labeled Graphs,
PR(4), No. 1, January 1972, pp. 5-17.
Elsevier DOI BibRef 7201

Fischler, M.A., and Elschlager, R.A.[Robert A.],
The Representation and Matching of Pictorial Structures,
TC(22), No. 1, January, 1973, pp. 67-92. BibRef 7301
And: CMetImAly77(31-56). Deformable Template. Early good paper using springs between nodes in the graph. BibRef

Fischler, M.A.,
On the Representation of Natural Scenes,
CVS78(47-52). BibRef 7800
Earlier:
Robot Vision: Sketching Natural Scenes,
ARPA96(879-890). Similar in concept to intrinsic images. Do not need exact data. BibRef

Firschein, O., and Fischler, M.A.,
Describing and Abstracting Pictorial Structures,
PR(3), No. 4, November 1971, pp. 421-434.
Elsevier DOI BibRef 7111

Firschein, O., Fischler, M.A.,
A study in descriptive representation of pictorial data,
PR(4), No. 4, December 1972, pp. 361-366.
Elsevier DOI 0309
Attempt at general descriptions for general analysis. BibRef

Ram, G.,
Analysis of Images Specified by Graphlike Descriptions,
CGIP(5), 1976, pp. 137-148. BibRef 7600

Cohen, B.L.,
A Powerful and Efficient Structural Pattern Recognition System,
AI(9), No. 3, December 1977, pp. 223-255.
Elsevier DOI BibRef 7712

Giustini, R.G., Levine, M.D., Malowany, A.S.,
Picture Generation Using Semantic Nets,
CGIP(7), No. 1, February 1978, pp. 1-29.
Elsevier DOI BibRef 7802

Itai, A., Rodeh, M., Tanimoto, S.L.,
Some Matching Problems for Bipartite Graphs,
JACM(25), 1978, pp. 517-525. BibRef 7800

Funt, B.V.[Brian V.],
Problem Solving with Diagrammatic Representations,
AI(13), No. 3, May 1980, pp. 201-230.
Elsevier DOI BibRef 8005
Earlier:
Whisper: A Problem-Solving System Utilizing Diagrams and a Parallel Processing Retina,
IJCAI77(459-464). BibRef

Levine, M.D., Ting, D.,
Intermediate Level Picture Interpretation Using Complete Two-Dimensional Models,
CGIP(16), No. 3, July 1981, pp. 185-209.
Elsevier DOI BibRef 8107

Kodratoff, Y.[Yves],
Generation and semantics of patterns in a discrete space,
CGIP(5), No. 4, December 1976, pp. 447-458.
Elsevier DOI 0501
BibRef

Kodratoff, Y.[Yves], Lemerle-Loisel, R.[Regine], Kodratoff, Y., Lemerle-Loisel, R.,
Learning Complex Structural Descriptions from Examples,
CVGIP(27), No. 3, September 1984, pp. 266-290.
Elsevier DOI BibRef 8409
Earlier: IJCAI81(141-143). BibRef

Krose, B.J.A.,
A Structure Description of Visual Information,
PRL(3), 1985, pp. 41-50. BibRef 8500

Werman, M., Peleg, S., Melter, R., and Kong, T.Y.,
Bipartite Graph Matching for Points on a Line or a Circle,
Algorithms(7), 1986, pp. 277-284. BibRef 8600

Niemann, H., Sagerer, G.F., Schroder, S., and Kummert, F.,
ERNEST: A Semantic Network System for Pattern Understanding,
PAMI(12), No. 9, September 1990, pp. 883-905.
IEEE DOI Discusses the graph structure for matching and how to use a general graph matching system. A lot is fairly standard, except that it is general. BibRef 9009

Bauckhage, C.[Christian], Kummert, F.[Franz], Sagerer, G.F.[Gerhard F.],
A Structural Framework for Assembly Modeling and Recognition,
CAIP03(49-56).
Springer DOI 0311
BibRef

Hanheide, M., Bauckhage, C., Sagerer, G.F.,
Memory consistency validation in a cognitive vision system,
ICPR04(II: 459-462).
IEEE DOI 0409
BibRef

Niemann, H., Sagerer, G.F., Eichhorn, W.,
Control Strategies in a Hierarchical Knowledge Structure,
PRAI(2), 1988, pp. 557-572. BibRef 8800

Niemann, H.,
A Homogeneous Architecture for Knowledge Based Image Understanding Systems,
CAIA85(88-93). BibRef 8500

Goel, A., Bylander, T.,
Computational feasibility of structured matching,
PAMI(11), No. 12, December 1989, pp. 1312-1316.
IEEE DOI 0401
BibRef

Suganuma, Y.[Yoshinori],
Learning Structures of Visual Patterns from Single Instances,
AI(50), No. 1, June 1991, pp. 1-36.
Elsevier DOI BibRef 9106

Blake, R.E.,
Partitioning Graph Matching with Constraints,
PR(27), No. 3, March 1994, pp. 439-446.
Elsevier DOI BibRef 9403

Caelli, T.M.[Terry M.], Kosinov, S.[Serhiy],
An Eigenspace Projection Clustering Method for Inexact Graph Matching,
PAMI(26), No. 4, April 2004, pp. 515-519.
IEEE Abstract. 0403
Spectral approach for graph matching. BibRef

Caelli, T.M.[Terry M.], Caetano, T.S.[Tiberio S.],
Graphical models for graph matching: Approximate models and optimal algorithms,
PRL(26), No. 3, February 2005, pp. 339-346.
Elsevier DOI 0501

See also Graphical Models and Point Pattern Matching. BibRef

Caetano, T.S., Caelli, T.M., Barone, D.A.C.,
Graphical models for graph matching,
CVPR04(II: 466-473).
IEEE DOI 0408
Probabilistic approach for graph matching. BibRef

Lu, J.F.[Jian-Feng], Caelli, T.M., Yang, J.Y.[Jing-Yu],
A graph decomposition approach to least squares attributed graph matching,
ICPR04(II: 471-474).
IEEE DOI 0409
BibRef

Caetano, T.S.[Tibério S.], McAuley, J.J.[Julian J.], Cheng, L.[Li], Le, Q.V.[Quoc V.], Smola, A.J.[Alex J.],
Learning Graph Matching,
PAMI(31), No. 6, June 2009, pp. 1048-1058.
IEEE DOI
PDF File. 0904
BibRef
Earlier: A1, A3, A4, A5, Only: ICCV07(1-8).
IEEE DOI
PDF File. 0710
Graph matching for point matching.
See also Graph Rigidity, Cyclic Belief Propagation, and Point Pattern Matching. BibRef

McAuley, J.J.[Julian J.], de Campos, T.[Teofilo], Caetano, T.S.[Tiberio S.],
Unified graph matching in Euclidean spaces,
CVPR10(1871-1878).
IEEE DOI 1006
BibRef

McAuley, J.J.[Julian J.], Ramisa, A.[Arnau], Caetano, T.S.[Tibério S.],
Optimization of Robust Loss Functions for Weakly-Labeled Image Taxonomies,
IJCV(104), No. 3, September 2013, pp. 343-361.
Springer DOI 1308
BibRef
Earlier:
Optimization of Robust Loss Functions for Weakly-Labeled Image Taxonomies: An ImageNet Case Study,
EMMCVPR11(355-368).
Springer DOI 1107
BibRef

Chen, L.B.[Long-Bin], McAuley, J.J.[Julian J.], Feris, R.S.[Rogerio S.], Caetano, T.S.[Tiberio S.], Turk, M.A.[Matthew A.],
Shape classification through structured learning of matching measures,
CVPR09(365-372).
IEEE DOI 0906
BibRef

de Piero, F.W., Trivedi, M.M., Serbin, S.,
Graph Matching Using a Direct Classification of Node Attendance,
PR(29), No. 6, June 1996, pp. 1031-1048.
Elsevier DOI 9606
BibRef

Tang, Y.C., Lee, C.S.G.,
Optimal Strategic Recognition of Objects Based on Candidate Discriminating Graph with Coordinated Sensors,
SMC(22), 1992, pp. 647-661. BibRef 9200

Cross, A.D.J., Wilson, R.C., Hancock, E.R.,
Inexact Graph Matching Using Genetic Search,
PR(30), No. 6, June 1997, pp. 953-970.
Elsevier DOI 9706
BibRef
Earlier:
Genetic Search for Structural Matching,
ECCV96(I:514-525).
Springer DOI BibRef

Cross, A.D.J.[Andrew D.J.], Hancock, E.R.[Edwin R.],
Graph Matching with a Dual-Step EM Algorithm,
PAMI(20), No. 11, November 1998, pp. 1236-1253.
IEEE DOI 9811
BibRef
Earlier:
Perspective matching using the EM algorithm,
CIAP97(I: 406-413).
Springer DOI 9709
BibRef

Wilson, R.C.[Richard C.], Cross, A.D.J.[Andrew D.J.], Hancock, E.R.[Edwin R.],
Structural Matching with Active Triangulations,
CVIU(72), No. 1, October 1998, pp. 21-38.
DOI Link BibRef 9810

Torsello, A.[Andrea], Hancock, E.R.[Edwin R.],
Learning Shape-Classes Using a Mixture of Tree-Unions,
PAMI(28), No. 6, June 2006, pp. 954-967.
IEEE DOI 0605
BibRef
Earlier:
Learning Mixtures of Weighted Tree-Unions by Minimizing Description Length,
ECCV04(Vol III: 13-25).
Springer DOI 0405
BibRef
Earlier:
Graph Clustering with Tree-Unions,
CAIP03(451-459).
Springer DOI 0311
BibRef
Earlier:
Shape-space from tree-union,
ICPR02(I: 188-191).
IEEE DOI 0211
edit operations produce the trees.
See also Skeletal Measure of 2D Shape Similarity, A. BibRef

Sagerer, G.F.[Gerhard F.], Niemann, H.[Heinrich],
Semantic Networks for Understanding Scenes,
Plenum1997. ISBN 0-306-45704-0. 512 pp. Segmentation, Knowledge representation, Judgment, Control, Acquisition of Knowledge, Explanation and User Interface, Applications. BibRef 9700

Niemann, H.,
Hierarchical Graphs in Pattern Analysis,
ICPR80(213-216). BibRef 8000

Bunke, H., Sagerer, G.F.,
Use and Representation of Knowledge in Image Understanding Based on Semantic Networks,
ICPR84(1135-1137). BibRef 8400

Finch, A.M.[Andrew M.], Wilson, R.C.[Richard C.], Hancock, E.R.[Edwin R.],
Symbolic graph matching with the EM algorithm,
PR(31), No. 11, November 1998, pp. 1777-1790.
Elsevier DOI BibRef 9811

Williams, M.L.[Mark L.], Wilson, R.C.[Richard C.], Hancock, E.R.[Edwin R.],
Deterministic search for relational graph matching,
PR(32), No. 7, July 1999, pp. 1255-1271.
Elsevier DOI BibRef 9907

Jiang, X.Y.[Xiao-Yi], Bunke, H.[Horst],
Optimal quadratic-time isomorphism of ordered graphs,
PR(32), No. 7, July 1999, pp. 1273-1283.
Elsevier DOI BibRef 9907

Pelillo, M.[Marcello], Siddiqi, K.[Kaleem], Zucker, S.W.[Steven W.],
Matching Hierarchical Structures Using Association Graphs,
PAMI(21), No. 11, November 1999, pp. 1105-1120.
IEEE DOI 9912
BibRef
Earlier: ECCV98(II: 3).
Springer DOI BibRef
And:
Attributed tree matching and maximum weight cliques,
CIAP99(1154-1159).
IEEE DOI 9909
When trees are hierarchical find maximal cliques may not work. Recast the matching problem as a quadratic program. BibRef

Pelillo, M.[Marcello], Siddiqi, K.[Kaleem], Zucker, S.W.[Steven W.],
Many-to-many Matching of Attributed Trees Using Association Graphs and Game Dynamics,
VF01(583 ff.).
Springer DOI 0209
BibRef

Pelillo, M.[Marcello],
Matching Free Trees, Maximal Cliques, and Monotone Game Dynamics,
PAMI(24), No. 11, November 2002, pp. 1535-1541.
IEEE Abstract. 0211
BibRef
Earlier: EMMCVPR01(423-437).
Springer DOI 0205
BibRef

Pelillo, M.[Marcello],
Replicator Equations, Maximal Cliques, and Graph Isomorphism,
NeuroComp(11), No. 9, 1999, pp. 1933-1955. Replicator Equations. BibRef 9900

Pelillo, M.[Marcello],
A Unifying Framework for Relational Structure Matching,
ICPR98(Vol II: 1316-1319).
IEEE DOI 9808
BibRef

Torsello, A.[Andrea], Hidovic-Rowe, D.[Dzena], Pelillo, M.[Marcello],
Polynomial-Time Metrics for Attributed Trees,
PAMI(27), No. 7, July 2005, pp. 1087-1099.
IEEE Abstract. 0506
BibRef
Earlier:
A Polynomial-Time Metric for Attributed Trees,
ECCV04(Vol IV: 414-427).
Springer DOI 0405
BibRef
And:
Four metrics for efficiently comparing attributed trees,
ICPR04(II: 467-470).
IEEE DOI 0409
Four distance measures centered around the notion of a maximal similarity common subtree.
See also game-theoretic approach to partial clique enumeration, A. BibRef

Torsello, A.[Andrea], Albarelli, A.[Andrea], Pelillo, M.[Marcello],
Matching Relational Structures using the Edge-Association Graph,
CIAP07(775-780).
IEEE DOI 0709
BibRef

Erdem, A.[Aykut], Pelillo, M.[Marcello],
Graph Transduction as a Non-cooperative Game,
GbRPR11(195-204).
Springer DOI 1105
BibRef

van Wyk, M.A.[Michaël A.], Durrani, T.S.[Tariq S.], van Wyk, B.J.[Barend J.],
A RKHS Interpolator-Based Graph Matching Algorithm,
PAMI(24), No. 7, July 2002, pp. 988-995.
IEEE Abstract. 0207
Graph matching for lines from aerial images. BibRef

Toudjeu, I.T.[Ignace Tchangou], van Wyk, B.J.[Barend Jacobus], van Wyk, M.A.[Michaël Antonie], van den Bergh, F.[Frans],
Global Image Feature Extraction Using Slope Pattern Spectra,
ICIAR08(xx-yy).
Springer DOI 0806
BibRef

van Wyk, M.A.[Michaël A.], Durrani, T.S.[Tariq S.],
A Framework for Multi-Scale and Hybrid RKHS-Based Approximators,
TSP(48), No. 12, 2000, pp. 3559-3568.
IEEE Top Reference. BibRef 0001

van Wyk, B.J., van Wyk, M.A.,
Kronecker product graph matching,
PR(36), No. 9, September 2003, pp. 2019-2030.
Elsevier DOI 0307
BibRef

Sangineto, E.[Enver],
An abstract representation of geometric knowledge for object classification,
PRL(24), No. 9-10, June 2003, pp. 1241-1250.
Elsevier DOI 0304
Efficient algorithm for constraint satisfaction. BibRef

He, L.[Lei], Han, C.Y.[Chia Y.], Everding, B.[Bryan], Wee, W.G.[William G.],
Graph matching for object recognition and recovery,
PR(37), No. 7, July 2004, pp. 1557-1560.
Elsevier DOI 0405
BibRef

Lopresti, D.P., Wilfong, G.,
A fast technique for comparing graph representations with applications to performance evaluation,
IJDAR(6), No. 4, April 2004, pp. 219-229.
Springer DOI 0406
Document analysis application. BibRef

Gori, M., Maggini, M., Sarti, L.,
Exact and Approximate Graph Matching Using Random Walks,
PAMI(27), No. 7, July 2005, pp. 1100-1111.
IEEE Abstract. 0506
BibRef
Earlier:
Graph matching using random walks,
ICPR04(III: 394-397).
IEEE DOI 0409
BibRef

Frey, B.J.[Brendan J.], Jojic, N.[Nebojsa],
A Comparison of Algorithms for Inference and Learning in Probabilistic Graphical Models,
PAMI(27), No. 9, September 2005, pp. 1392-1416.
IEEE DOI 0508
Graph models of the image. BibRef

Naik, S.K.[Sarif Kumar], Murthy, C.A.,
Distinct Multicolored Region Descriptors for Object Recognition,
PAMI(29), No. 7, July 2007, pp. 1291-1296.
IEEE DOI 0706
Color features of regions for recognition. BibRef

Kohli, P.[Pushmeet], Ladický, L.[Lubor], Torr, P.H.S.[Philip H.S.],
Robust Higher Order Potentials for Enforcing Label Consistency,
IJCV(82), No. 3, May 2009, pp. xx-yy.
Springer DOI 0903
BibRef
Earlier: CVPR08(1-8).
IEEE DOI 0806
BibRef

Alahari, K.[Karteek], Russell, C.[Chris], Torr, P.H.S.[Philip H. S.],
Efficient piecewise learning for conditional random fields,
CVPR10(895-901).
IEEE DOI 1006
BibRef

Kohli, P.[Pushmeet], Kumar, M.P.[M. Pawan], Torr, P.H.S.[Philip H.S.],
P3 and Beyond: Move Making Algorithms for Solving Higher Order Functions,
PAMI(31), No. 9, September 2009, pp. 1645-1656.
IEEE DOI 0907
BibRef
Earlier:
P3 and Beyond: Solving Energies with Higher Order Cliques,
CVPR07(1-8).
IEEE DOI 0706
Energy minimization for texture segmentation. Extend class for computing in polynomial time. BibRef

Zheng, K.J.[Kai-Jie], Peng, J.G.[Ji-Gen], Ying, S.H.[Shi-Hui],
A New Approach to Weighted Graph Matching,
IEICE(E92-D), No. 8, August 2009, pp. 1580-1583.
WWW Link. 0909
BibRef

Zaslavskiy, M.[Mikhail], Bach, F.[Francis], Vert, J.P.[Jean-Philippe],
A Path Following Algorithm for the Graph Matching Problem,
PAMI(31), No. 12, December 2009, pp. 2227-2242.
IEEE DOI 0911
BibRef
Earlier:
A Path Following Algorithm for Graph Matching,
ICISP08(329-337).
Springer DOI 0807
Weighted graph matching BibRef

Harchaoui, Z.[Zaid], Bach, F.[Francis],
Image Classification with Segmentation Graph Kernels,
CVPR07(1-8).
IEEE DOI 0706
Kernels based on graph matching. BibRef

Cheng, B., Yang, J., Yan, S., Fu, Y., Huang, T.S.,
Learning With L1-Graph for Image Analysis,
IP(19), No. 4, April 2010, pp. 858-866.
IEEE DOI 1003
Graph construction for learning and clustering. BibRef

Lin, L.[Liang], Liu, X.B.[Xiao-Bai], Zhu, S.C.[Song-Chun],
Layered Graph Matching with Composite Cluster Sampling,
PAMI(32), No. 8, August 2010, pp. 1426-1442.
IEEE DOI 1007
Integrate graph partitioning and matching. Find unknown number of corresponding graph structures in two images. BibRef

Lin, L.[Liang], Zeng, K.[Kun], Liu, X.B.[Xiao-Bai], Zhu, S.C.[Song-Chun],
Layered graph matching by composite cluster sampling with collaborative and competitive interactions,
CVPR09(1351-1358).
IEEE DOI 0906
BibRef

Chertok, M.[Michael], Keller, Y.[Yosi],
Efficient High Order Matching,
PAMI(32), No. 12, December 2010, pp. 2205-2215.
IEEE DOI 1011
measure scores of matching more than 2 pairs of points at a time. Graph matching.
See also Probabilistic graph and hypergraph matching. BibRef

Darom, T.[Tal], Keller, Y.[Yosi],
Spectral Analysis Driven Sparse Matching of 3D Shapes,
3DOR12(59-62)
PDF File.
DOI Link 1301
BibRef

Huang, Y.[Yuchi], Liu, Q.S.[Qing-Shan], Lv, F.J.[Feng-Jun], Gong, Y.H.[Yi-Hong], Metaxas, D.N.[Dimitris N.],
Unsupervised Image Categorization by Hypergraph Partition,
PAMI(33), No. 6, June 2011, pp. 1266-1273.
IEEE DOI 1105
Images containing objects are vertices, clustering via hypergraph partition. Select ROI then edges based on shapes. BibRef

Demirci, M.F.[M. Fatih], Osmanlioglu, Y.[Yusuf], Shokoufandeh, A.[Ali], Dickinson, S.J.[Sven J.],
Efficient Many-to-Many Feature Matching under the L1 Norm,
CVIU(115), No. 7, July 2011, pp. 976-983.
Elsevier DOI 1106
BibRef
Earlier: A1, A2, Only:
Many-to-Many Matching under the L1 Norm,
CIAP09(787-796).
Springer DOI 0909
Distortion-free metric embedding; Earth Mover's Distance; Many-to-many matching; Object recognition Graph matching. BibRef

Wang, J.Y.[Jing-Yan], Li, Y.P.[Yong-Ping], Bai, X.[Xiang], Zhang, Y.[Ying], Wang, C.[Chao], Tang, N.[Ning],
Learning context-sensitive similarity by shortest path propagation,
PR(44), No. 10-11, October-November 2011, pp. 2367-2374.
Elsevier DOI 1101
Shape retrieval; Contextual similarity learning; Graph transduction; Shortest path propagation BibRef

Duchenne, O.[Olivier], Bach, F.[Francis], Kweon, I.S.[In-So], Ponce, J.[Jean],
A Tensor-Based Algorithm for High-Order Graph Matching,
PAMI(33), No. 12, December 2011, pp. 2383-2395.
IEEE DOI 1110
BibRef
Earlier: CVPR09(1980-1987).
IEEE DOI 0906
Award, CVPR, Student, HM. Establish correspondence using higher level (more than pairwise). BibRef

Cho, M.S.[Min-Su], Alahari, K.[Karteek], Ponce, J.[Jean],
Learning Graphs to Match,
ICCV13(25-32)
IEEE DOI 1403
feature correspondence BibRef

Duchenne, O.[Olivier], Joulin, A.[Armand], Ponce, J.[Jean],
A graph-matching kernel for object categorization,
ICCV11(1792-1799).
IEEE DOI 1201
BibRef

Liu, Z.Y.[Zhi-Yong], Qiao, H.[Hong], Xu, L.[Lei],
An Extended Path Following Algorithm for Graph-Matching Problem,
PAMI(34), No. 7, July 2012, pp. 1451-1456.
IEEE DOI 1205
Solve matching on directed graphs using path following (previously for undirected graphs).
See also Path Following Algorithm for the Graph Matching Problem, A. BibRef

Yu, J., Tao, D., Wang, M.,
Adaptive Hypergraph Learning and its Application in Image Classification,
IP(21), No. 7, July 2012, pp. 3262-3272.
IEEE DOI 1206
BibRef

Molina-Abril, H.[Helena], Real Jurado, P.[Pedro],
Homological optimality in Discrete Morse Theory through chain homotopies,
PRL(33), No. 11, 1 August 2012, pp. 1501-1506.
Elsevier DOI 1206
BibRef
Earlier:
A Homological-Based Description of Subdivided n-D Objects,
CAIP11(I: 42-50).
Springer DOI 1109
BibRef
Earlier:
Homological Computation Using Spanning Trees,
CIARP09(272-278).
Springer DOI 0911
BibRef
Earlier:
Advanced Homology Computation of Digital Volumes Via Cell Complexes,
SSPR08(361-371).
Springer DOI 0812
Discrete Morse Theory; Gradient vector field; Cell complex; Integral-chain complex; Chain homotopy; Graph BibRef

Real Jurado, P.[Pedro], Molina-Abril, H.[Helena], Kropatsch, W.G.[Walter G.],
Homological Tree-Based Strategies for Image Analysis,
CAIP09(326-333).
Springer DOI 0909
BibRef

Real Jurado, P.[Pedro], Molina-Abril, H.[Helena],
Cell AT-Models for Digital Volumes,
GbRPR09(314-323).
Springer DOI 0905
BibRef

Pacheco, A.[Ana], Real Jurado, P.[Pedro],
Associating Cell Complexes to Four Dimensional Digital Objects,
DGCI11(104-115).
Springer DOI 1104
BibRef
Earlier:
Getting Topological Information for a 80-Adjacency Doxel-Based 4D Volume through a Polytopal Cell Complex,
CIARP09(279-286).
Springer DOI 0911
BibRef

Taylor, K.M.[Kye M.], Meyer, F.G.[François G.],
A Random Walk on Image Patches,
SIIMS(5), No. 2, 2012, pp. 688-725.
DOI Link 1206
Analyze algorithms that organize patches using graph-based metrics. BibRef

Tuia, D., Munoz-Mari, J., Gomez-Chova, L., Malo, J.,
Graph Matching for Adaptation in Remote Sensing,
GeoRS(51), No. 1, January 2013, pp. 329-341.
IEEE DOI 1301
BibRef

de Ita Luna, G.[Guillermo], Castillo, J.A.[Javier A.],
Recognizing 3-colorings cycle-patterns on graphs,
PRL(34), No. 4, 1 March 2013, pp. 433-438.
Elsevier DOI 1302
Graph coloring; 3-Coloring; Satisfiability problem; Recognizing cycle-patterns BibRef

Spears, W.M.[William M.], Prager, S.D.[Steven D.],
Evolutionary search for understanding movement dynamics on mixed networks,
GeoInfo(17), No. 2, April 2013, pp. 353-385.
Springer DOI 1304
Paths through networks. BibRef

Daneshgar, A., Javadi, R., Shariat Razavi, S.B.,
Clustering and outlier detection using isoperimetric number of trees,
PR(46), No. 12, 2013, pp. 3371-3382.
Elsevier DOI 1308
Isoperimetric constant. Graph based clustering. BibRef

Xu, J.J.[Jie-Jun], Jagadeesh, V., Manjunath, B.S.,
Multi-Label Learning With Fused Multimodal Bi-Relational Graph,
MultMed(16), No. 2, February 2014, pp. 403-412.
IEEE DOI 1404
graph theory BibRef

McGreggor, K.[Keith], Kunda, M.[Maithilee], Goel, A.[Ashok],
Fractals and Ravens,
AI(215), No. 1, 2014, pp. 1-23.
Elsevier DOI 1408
Analogy. Reasoning. BibRef

Dazzi, E.[Estephan], de Campos, T.[Teofilo], Cesar, Jr., R.M.[Roberto M.],
Improved Object Matching Using Structural Relations,
SSSPR14(444-453).
Springer DOI 1408
BibRef

Jeong, H., Yoon, B.J.,
Effective Estimation of Node-to-Node Correspondence Between Different Graphs,
SPLetters(22), No. 6, June 2015, pp. 661-665.
IEEE DOI 1411
Biological system modeling BibRef

Czioska, P.[Paul], Thiemann, F.[Frank], Sester, M.[Monika], Giese, R.[Robin], Vogt, H.[Hermann],
An Algorithm to Generate a Simplified Railway Network through Generalization,
PFG(2015), No. 1, 2015, pp. 95-104.
DOI Link 1503
BibRef

Qi, X.Q.[Xing-Qin], Fuller, E.[Edgar], Luo, R.[Rong], Zhang, C.Q.[Cun-Quan],
A novel centrality method for weighted networks based on the Kirchhoff polynomial,
PRL(58), No. 1, 2015, pp. 51-60.
Elsevier DOI 1505
Centrality method. Importance of vertex in network. BibRef

Gudivada, S.[Sravan], Bors, A.G.[Adrian G.],
Ortho-diffusion decompositions of graph-based representation of images,
PR(48), No. 12, 2015, pp. 4097-4115.
Elsevier DOI 1509
BibRef
And:
Robust Learning from Ortho-Diffusion Decompositions,
CAIP15(I:546-557).
Springer DOI 1511
BibRef
Earlier:
Orthonormal Diffusion Decompositions of Images for Optical Flow Estimation,
CAIP13(II:241-249).
Springer DOI 1311
Ortho-diffusion decompositions BibRef

Halappanavar, M., Pothen, A., Azad, A., Manne, F., Langguth, J., Khan, A.,
Codesign Lessons Learned from Implementing Graph Matching on Multithreaded Architectures,
Computer(48), No. 8, August 2015, pp. 46-55.
IEEE DOI 1509
graph theory BibRef

Wang, S.[Song], Guo, X.[Xin], Mu, X.M.[Xiao-Min], Huo, Y.[Yahong], Qi, L.[Lin],
Advanced weight graph transformation matching algorithm,
IET-CV(9), No. 6, 2015, pp. 960-966.
DOI Link 1512
graph theory BibRef

Xie, X.H.[Xiao-Hua], Yang, L.X.[Ling-Xiao], Zheng, W.S.[Wei-Shi],
Learning object-specific DAGs for multi-label material recognition,
CVIU(143), No. 1, 2016, pp. 183-190.
Elsevier DOI 1601
Directed Acyclic Graph. Material recognition BibRef

Zhang, Q.S.[Quan-Shi], Song, X.[Xuan], Shao, X.W.[Xiao-Wei], Zhao, H.J.[Hui-Jing], Shibasaki, R.[Ryosuke],
Object Discovery: Soft Attributed Graph Mining,
PAMI(38), No. 3, March 2016, pp. 532-545.
IEEE DOI 1602
BibRef
Earlier:
Attributed Graph Mining and Matching: An Attempt to Define and Extract Soft Attributed Patterns,
CVPR14(1394-1401)
IEEE DOI 1409
Data mining. Attributed Relational Graphs; Graph Matching; Graph Mining BibRef

Zhang, Q.S.[Quan-Shi], Song, X.[Xuan], Yang, Y.[Yu], Ma, H.T.[Hao-Tian], Shibasaki, R.[Ryosuke],
Visual graph mining for graph matching,
CVIU(178), 2019, pp. 16-29.
Elsevier DOI 1812
BibRef

Yan, J.C.[Jun-Chi], Cho, M., Zha, H.Y.[Hong-Yuan], Yang, X.K.[Xiao-Kang], Chu, S.M.Y.[Stephen Ming-Yu],
Multi-Graph Matching via Affinity Optimization with Graduated Consistency Regularization,
PAMI(38), No. 6, June 2016, pp. 1228-1242.
IEEE DOI 1605
Accuracy BibRef

Yan, J.C.[Jun-Chi], Li, Y.[Yin], Liu, W.[Wei], Zha, H.Y.[Hong-Yuan], Yang, X.K.[Xiao-Kang], Chu, S.M.Y.[Stephen Ming-Yu],
Graduated Consistency-Regularized Optimization for Multi-graph Matching,
ECCV14(I: 407-422).
Springer DOI 1408
BibRef

Yan, J.C.[Jun-Chi], Xu, H.T.[Hong-Teng], Zha, H.Y.[Hong-Yuan], Yang, X.K.[Xiao-Kang], Liu, H.X.[Huan-Xi], Chu, S.[Stephen],
A Matrix Decomposition Perspective to Multiple Graph Matching,
ICCV15(199-207)
IEEE DOI 1602
Computer vision BibRef

Wang, P.[Peng], Shen, C.H.[Chun-Hua], van den Hengel, A.J.[Anton J.], Torr, P.H.S.[Philip H. S.],
Efficient Semidefinite Branch-and-Cut for MAP-MRF Inference,
IJCV(117), No. 3, May 2016, pp. 269-289.
Springer DOI 1605
BibRef
Earlier: A1, A2, A3, Only:
Efficient SDP inference for fully-connected CRFs based on low-rank decomposition,
CVPR15(3222-3231)
IEEE DOI 1510
BibRef

Itoh, H.[Hayato], Imiya, A.[Atsushi], Sakai, T.[Tomoya],
Dimension Reduction and Construction of Feature Space for Image Pattern Recognition,
JMIV(56), No. 1, September 2016, pp. 1-31.
WWW Link. 1605
BibRef
Earlier:
Discriminative Properties in Directional Distributions for Image Pattern Recognition,
PSIVT15(617-630).
Springer DOI 1602
BibRef
Earlier: A3, A1, A2:
Multi-label Classification for Image Annotation via Sparse Similarity Voting,
Subspace10(344-353).
Springer DOI 1109

See also Volumetric Image Pattern Recognition Using Three-Way Principal Component Analysis. BibRef

Itoh, H.[Hayato], Imiya, A.[Atsushi], Sakai, T.[Tomoya],
Multilinear Methods for Spatio-Temporal Image Recognition,
CAIP17(I: 148-159).
Springer DOI 1708
BibRef

Itoh, H.[Hayato], Sakai, T.[Tomoya], Kawamoto, K.[Kazuhiko], Imiya, A.[Atsushi],
Topology-Preserving Dimension-Reduction Methods for Image Pattern Recognition,
SCIA13(195-204).
Springer DOI 1311
BibRef

Igelmo, M.[Manuel], Sanfeliu, A.[Alberto],
Filtering graphs to check isomorphism and extracting mapping by using the Conductance Electrical Model,
PR(58), No. 1, 2016, pp. 68-82.
Elsevier DOI 1606
Graph isomorphism BibRef

Igelmo, M.[Manuel], Sanfeliu, A.[Alberto],
Compact Form of the Pseudo-Inverse Matrix in the Approximation of a Star Graph Using the Conductance Electrical Model (CEM),
SSSPR12(539-547).
Springer DOI 1211
BibRef

Igelmo, M.[Manuel], Sanfeliu, A.[Alberto], Ferrer, M.[Miquel],
A Conductance Electrical Model for Representing and Matching Weighted Undirected Graphs,
ICPR10(958-961).
IEEE DOI 1008
BibRef

Yang, X.[Xu], Qiao, H.[Hong], Liu, Z.Y.[Zhi-Yong],
Point correspondence by a new third order graph matching algorithm,
PR(65), No. 1, 2017, pp. 108-118.
Elsevier DOI 1702
Graph matching BibRef

Luo, S.[Sheng], Zhou, H.M.[Hong-Ming], Xu, J.H.[Jing-Hua], Zhang, S.Y.[Shu-You],
Matching images based on consistency graph and region adjacency graphs,
SIViP(11), No. 3, March 2017, pp. 501-508.
WWW Link. 1702
BibRef

Bujoreanu, D.[Denis], Dorez, H.[Hugo], Boutegrabet, W.[Warda], Moussata, D.[Driffa], Sablong, R.[Raphaël], Rousseau, D.[David],
Robust graph representation of images with underlying structural networks. Application to the classification of vascular networks of mice's colon,
PRL(87), No. 1, 2017, pp. 29-37.
Elsevier DOI 1703
Graph-based image representation BibRef

Osmanlioglu, Y.[Yusuf], Shokoufandeh, A.[Ali],
Multilayer matching of metric structures using hierarchically well-separated trees,
PRL(87), No. 1, 2017, pp. 63-70.
Elsevier DOI 1703
Hierarchically well-separated tree. One-to-one matching of features may not always be possible. BibRef

Yin, R.J.[Ru-Jie], Gao, T.[Tingran], Lu, Y.M.[Yue M.], Daubechies, I.[Ingrid],
A Tale of Two Bases: Local-Nonlocal Regularization on Image Patches with Convolution Framelets,
SIIMS(10), No. 2, 2017, pp. 711-750.
DOI Link 1708
Local and nonlocal patch descriptions. BibRef

Park, H.M.[Han-Mu], Yoon, K.J.[Kuk-Jin],
Multi-Attributed Graph Matching With Multi-Layer Graph Structure and Multi-Layer Random Walks,
IP(27), No. 5, May 2018, pp. 2314-2325.
IEEE DOI 1804
BibRef
Earlier:
Multi-attributed Graph Matching with Multi-layer Random Walks,
ECCV16(III: 189-204).
Springer DOI 1611
graph theory, random processes, conventional graph matching algorithms, multiple attributes BibRef

Park, H.M.[Han-Mu], Yoon, K.J.[Kuk-Jin],
Exploiting multi-layer graph factorization for multi-attributed graph matching,
PRL(127), 2019, pp. 85-93.
Elsevier DOI 1911
Multi-attributed graph matching, Multi-layer structure, Matrix factorization, Path following BibRef

Park, H.M.[Han-Mu], Yoon, K.J.[Kuk-Jin],
Consistent multiple graph matching with multi-layer random walks synchronization,
PRL(127), 2019, pp. 76-84.
Elsevier DOI 1911
BibRef

Cheung, G.[Gene], Magli, E.[Enrico], Tanaka, Y.C.[Yui-Chi], Ng, M.K.[Michael K.],
Graph Spectral Image Processing,
PIEEE(106), No. 5, May 2018, pp. 907-930.
IEEE DOI 1805
Digital images, Eigenvalues and eigenfunctions, Graph theory, Image coding, Image edge detection, Laplace equations, image processing BibRef

Liu, L., Nie, F., Wiliem, A., Li, Z., Zhang, T.[Teng], Lovell, B.C.,
Multi-Modal Joint Clustering With Application for Unsupervised Attribute Discovery,
IP(27), No. 9, September 2018, pp. 4345-4356.
IEEE DOI 1807
BibRef
Earlier: A1, A2, A5, A3, A6, Only:
Unsupervised automatic attribute discovery method via multi-graph clustering,
ICPR16(1713-1718)
IEEE DOI 1705
graph theory, image representation, pattern clustering, unsupervised learning, automatic attribute discovery method, unsupervised automatic attribute discovery. Binary codes, Iterative methods, Labeling, Linear programming, Measurement, Pathology, Visualization BibRef

Lu, B.B.[Bin-Bin], Sun, H.B.[Hua-Bo], Harris, P.[Paul], Xu, M.Z.[Miao-Zhong], Charlton, M.[Martin],
Shp2graph: Tools to Convert a Spatial Network into an Igraph Graph in R,
IJGI(7), No. 8, 2018, pp. xx-yy.
DOI Link 1809
BibRef

Dazzi, E.[Estephan], de Campos, T.[Teofilo], Hilton, A.[Adrian], Cesar, Jr., R.M.[Roberto M.],
Scalable object instance recognition based on keygraph matching,
PRL(114), 2018, pp. 53-62.
Elsevier DOI 1811
Local invariant features, Semi-local graph matching, Graph topological properties BibRef

Shi, D.[Dan], Zhu, L.[Lei], Cheng, Z.Y.[Zhi-Yong], Li, Z.H.[Zhi-Hui], Zhang, H.X.[Hua-Xiang],
Unsupervised multi-view feature extraction with dynamic graph learning,
JVCIR(56), 2018, pp. 256-264.
Elsevier DOI 1811
Multi-view feature extraction, Intrinsic sample relations, Dynamic graph learning BibRef

Gligorijevic, V.[Vladimir], Panagakis, Y.[Yannis], Zafeiriou, S.P.[Stefanos P.],
Non-Negative Matrix Factorizations for Multiplex Network Analysis,
PAMI(41), No. 4, April 2019, pp. 928-940.
IEEE DOI 1903
BibRef
Earlier:
Fusion and community detection in multi-layer graphs,
ICPR16(1327-1332)
IEEE DOI 1705
Multiplexing, Matrix decomposition, Feature extraction, Biology, Clustering algorithms, Partitioning algorithms, Tools, network integration. Image edge detection, Linear programming, Optimization, Robustness, Symmetric matrices. BibRef

Jin, T., Ji, R., Gao, Y., Sun, X., Zhao, X., Tao, D.,
Correntropy-Induced Robust Low-Rank Hypergraph,
IP(28), No. 6, June 2019, pp. 2755-2769.
IEEE DOI 1905
data structures, Gaussian noise, graph theory, image classification, image representation, hyperedge BibRef

Li, C., Zhu, C., Zhang, J., Luo, B., Wu, X., Tang, J.,
Learning Local-Global Multi-Graph Descriptors for RGB-T Object Tracking,
CirSysVideo(29), No. 10, October 2019, pp. 2913-2926.
IEEE DOI 1910
graph theory, image colour analysis, image representation, infrared imaging, learning (artificial intelligence), fast optimization BibRef

Cantero, S.V.A.B., Gonçalves, D.N., dos Santos Scabini, L.F., Gonçalves, W.N.,
Importance of Vertices in Complex Networks Applied to Texture Analysis,
Cyber(50), No. 2, February 2020, pp. 777-786.
IEEE DOI 1912
Web pages, Level measurement, Complex networks, Topology, Computational modeling, Cybernetics, Computer vision, texture analysis BibRef

Kang, Z., Pan, H., Hoi, S.C.H., Xu, Z.,
Robust Graph Learning From Noisy Data,
Cyber(50), No. 5, May 2020, pp. 1833-1843.
IEEE DOI 2005
Noise measurement, Adaptation models, Laplace equations, Manifolds, Task analysis, Reliability, Data models, Clustering, similarity measure BibRef

Anderson, T.[Taylor], Dragicevic, S.[Suzana],
Representing Complex Evolving Spatial Networks: Geographic Network Automata,
IJGI(9), No. 4, 2020, pp. xx-yy.
DOI Link 2005
BibRef

Wang, J.J.[Jian-Jia], Wilson, R.C.[Richard C.], Hancock, E.R.[Edwin R.],
Directed and undirected network evolution from Euler-Lagrange dynamics,
PRL(134), 2020, pp. 135-144.
Elsevier DOI 2005
BibRef
Earlier:
Directed Graph Evolution from Euler-Lagrange Dynamics,
ICPR18(448-453)
IEEE DOI 1812
Dynamic networks, Euler-Lagrange equation, Approximate von neumann entropy. Entropy, Mathematical model, Correlation, Data models, Complex networks, Pattern recognition, Computational modeling BibRef

Brun, L.[Luc], Foggia, P.[Pasquale], Vento, M.[Mario],
Trends in graph-based representations for Pattern Recognition,
PRL(134), 2020, pp. 3-9.
Elsevier DOI 2005
Graph-based representations, Graph matching, Graph edit distance, Graph kernels BibRef

Martineau, M.[Maxime], Raveaux, R.[Romain], Conte, D.[Donatello], Venturini, G.[Gilles],
Learning error-correcting graph matching with a multiclass neural network,
PRL(134), 2020, pp. 68-76.
Elsevier DOI 2005
Learning graph matching, Graph classification, Graph edit distance BibRef

Santacruz, P.[Pep], Serratosa, F.[Francesc],
Error-tolerant graph matching in linear computational cost using an initial small partial matching,
PRL(134), 2020, pp. 10-19.
Elsevier DOI 2005
Graph Edit Distance, Sub-optimal algorithm, Linear computational cost BibRef

Santacruz, P.[Pep], Algabli, S.[Shaima], Serratosa, F.[Francesc],
Node Matching Computation Between Two Large Graphs in Linear Computational Cost,
GbRPR17(143-153).
Springer DOI 1706
BibRef

Algabli, S.[Shaima], Serratosa, F.[Francesc],
Learning Graph Matching Substitution Weights based on a Linear Regression,
ICPR21(53-58)
IEEE DOI 2105
Runtime, Databases, Linear regression, Buildings, Euclidean distance, Pattern recognition, Testing, Attributed graphs, linear regression BibRef

Bougleux, S.[Sébastien], Gaüzère, B.[Benoit], Blumenthal, D.B.[David B.], Brun, L.[Luc],
Fast linear sum assignment with error-correction and no cost constraints,
PRL(134), 2020, pp. 37-45.
Elsevier DOI 2005
Inexact graph matching, Linear assignment, Graph edit distance BibRef

Daller, É.[Évariste], Bougleux, S.[Sébastien], Brun, L.[Luc], Lézoray, O.[Olivier],
Local Patterns and Supergraph for Chemical Graph Classification with Convolutional Networks,
SSSPR18(97-106).
Springer DOI 1810
BibRef

Morimitsu, A.[Alexandre], Passat, N.[Nicolas], Alves, W.A.L.[Wonder A.L.], Hashimoto, R.F.[Ronaldo F.],
Efficient component-hypertree construction based on hierarchy of partitions,
PRL(135), 2020, pp. 30-37.
Elsevier DOI 2006
Mathematical morphology, Component-hypertree, Component tree, Connected operators, Hierarchy of partitions BibRef

Lyu, X.[Xiang], Sun, W.W.[Will Wei], Wang, Z.R.[Zhao-Ran], Liu, H.[Han], Yang, J.[Jian], Cheng, G.[Guang],
Tensor Graphical Model: Non-Convex Optimization and Statistical Inference,
PAMI(42), No. 8, August 2020, pp. 2024-2037.
IEEE DOI 2007
Graphical models, Estimation, Convergence, Testing, Sparse matrices, Covariance matrices, Asymptotic normality, hypothesis testing, rate of convergence BibRef

Lee, S.H.[Se-Hyung], Lim, J.W.[Jong-Woo], Suh, I.H.[Il Hong],
Progressive Feature Matching: Incremental Graph Construction and Optimization,
IP(29), 2020, pp. 6992-7005.
IEEE DOI 2007
Feature extraction, Optimization, Approximation algorithms, Computational modeling, Clustering algorithms, Markov processes, progressive optimization BibRef

Colonnese, S.[Stefania], di Lorenzo, P.[Paolo], Cattai, T.[Tiziana], Scarano, G.[Gaetano], de Vico Fallani, F.[Fabrizio],
A Joint Markov Model for Communities, Connectivity and Signals Defined Over Graphs,
SPLetters(27), 2020, pp. 1160-1164.
IEEE DOI 2007
Graph signal processing, markov random field, graph community, graph learning, graph signal denoising BibRef

Xu, L.X.[Li-Xiang], Bai, L.[Lu], Jiang, X.Y.[Xiao-Yi], Tan, M.[Ming], Zhang, D.Q.[Dao-Qiang], Luo, B.[Bin],
Deep Rényi entropy graph kernel,
PR(111), 2021, pp. 107668.
Elsevier DOI 2012
Shannon entropy, Rényi entropy, Deep representation, Graph kernel, Graph classification BibRef

Hu, F.Y.[Fen-Yu], Zhu, Y.Q.[Yan-Qiao], Wu, S.[Shu], Huang, W.R.[Wei-Ran], Wang, L.[Liang], Tan, T.N.[Tie-Niu],
GraphAIR: Graph representation learning with neighborhood aggregation and interaction,
PR(112), 2021, pp. 107745.
Elsevier DOI 2102
Graph representation learning, Neighborhood aggregation, Graph neural networks, Neighborhood interaction, Link prediction BibRef

Xu, N.[Ning], Liu, A.A.[An-An], Wong, Y.K.[Yong-Kang], Nie, W.Z.[Wei-Zhi], Su, Y.T.[Yu-Ting], Kankanhalli, M.[Mohan],
Scene Graph Inference via Multi-Scale Context Modeling,
CirSysVideo(31), No. 3, March 2021, pp. 1031-1041.
IEEE DOI 2103
Visualization, Context modeling, Proposals, Feature extraction, Semantics, Head, Safety, Scene graph, context-fused inference, multi-scale context BibRef

Zhu, S.B.[Shuai-Bing], Zhou, J.[Jin], Chen, G.R.[Guan-Rong], Lu, J.A.[Jun-An],
A New Method for Topology Identification of Complex Dynamical Networks,
Cyber(51), No. 4, April 2021, pp. 2224-2231.
IEEE DOI 2103
Topology, Synchronization, Couplings, Complex networks, Delays, Artificial neural networks, Complex network, topology identification BibRef

Li, J.[Jing], Wang, X.T.[Xuan-Tong], Zhang, T.[Tong],
Sequence-based centrality measures in maritime transportation networks,
IET-ITS(14), No. 14, 27 December 2020, pp. 2042-2051.
DOI Link 2103
BibRef

Qin, C.[Cao], Zhang, Y.Z.[Yun-Zhou], Liu, Y.D.[Ying-Da], Lv, G.H.[Guang-Hao],
Semantic loop closure detection based on graph matching in multi-objects scenes,
JVCIR(76), 2021, pp. 103072.
Elsevier DOI 2104
Loop closure detection, Object detection, Semantic, Simultaneous localization and mapping (SLAM), Graph matching BibRef

He, J.F.[Jian-Feng], Zhang, T.Z.[Tian-Zhu], Zheng, Y.[Yuhui], Xu, M.L.[Ming-Liang], Zhang, Y.D.[Yong-Dong], Wu, F.[Feng],
Consistency Graph Modeling for Semantic Correspondence,
IP(30), 2021, pp. 4932-4946.
IEEE DOI 2106
Semantics, Feature extraction, Solid modeling, Clutter, Image edge detection, Task analysis, Strain, cycle consistency BibRef

Hao, X.J.[Xue-Jie], Ji, Z.[Zheng], Li, X.H.[Xiu-Hong], Yin, L.Y.[Lize-Yan], Liu, L.[Lu], Sun, M.Y.[Mei-Ying], Liu, Q.[Qiang], Yang, R.[Rongjin],
Construction and Application of a Knowledge Graph,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef


Vandaele, R.[Robin], Saeys, Y.[Yvan], De Bie, T.[Tijl],
Graph Approximations to Geodesics on Metric Graphs,
ICPR21(7328-7334)
IEEE DOI 2105
Manifolds, Bridges, Visualization, Machine learning, Extraterrestrial measurements BibRef

Chen, C.[Chaofan],
A General Model for Learning Node and Graph Representations Jointly,
ICPR21(2867-2873)
IEEE DOI 2105
Correlation, Computational modeling, Benchmark testing, Feature extraction, Routing, Task analysis, deep learning BibRef

Wang, W.B.[Wen-Bin], Wang, R.P.[Rui-Ping], Shan, S.G.[Shi-Guang], Chen, X.L.[Xi-Lin],
Sketching Image Gist: Human-Mimetic Hierarchical Scene Graph Generation,
ECCV20(XIII:222-239).
Springer DOI 2011
BibRef

Manandhar, D.[Dipu], Ruta, D.[Dan], Collomosse, J.[John],
Learning Structural Similarity of User Interface Layouts Using Graph Networks,
ECCV20(XXII:730-746).
Springer DOI 2011
Code, Graph Matching.
WWW Link. BibRef

Shekkizhar, S., Ortega, A.,
Efficient Graph Construction For Image Representation,
ICIP20(1956-1960)
IEEE DOI 2011
Kernel, Windows, Image edge detection, Image representation, Adaptation models, Image representation, Graph Signal Processing. BibRef

Abelé, R., Damoiseaux, J.L., Fronte, D., Liardet, P.Y., Boï, J.M., Merad, D.,
Graph Matching Applied For Textured Pattern Recognition,
ICIP20(1451-1455)
IEEE DOI 2011
Integrated circuits, Image edge detection, Noise measurement, Robustness, Shape, Tensile stress, Labeling, Graph matching, superincreasing series BibRef

Zareian, A.[Alireza], Karaman, S.[Svebor], Chang, S.F.[Shih-Fu],
Bridging Knowledge Graphs to Generate Scene Graphs,
ECCV20(XXIII:606-623).
Springer DOI 2011
BibRef

Li, Y.Z.[Yong-Zhi], Zhang, D.[Duo], Mu, Y.D.[Ya-Dong],
Visual-Semantic Matching by Exploring High-Order Attention and Distraction,
CVPR20(12783-12792)
IEEE DOI 2008
Semantics, Visualization, Task analysis, Computational modeling, Image edge detection, Proposals BibRef

Zhang, L.[Li], Xu, D.[Dan], Arnab, A.[Anurag], Torr, P.H.S.[Philip H.S.],
Dynamic Graph Message Passing Networks,
CVPR20(3723-3732)
IEEE DOI 2008
Structural information. Message passing, Task analysis, Computational modeling, Convolution, Neural networks, Computer vision, Context modeling BibRef

Yu, T., Yan, J., Li, B.,
Determinant Regularization for Gradient-Efficient Graph Matching,
CVPR20(7121-7130)
IEEE DOI 2008
Optimization, Entropy, Tensile stress, Task analysis, Benchmark testing, Graphics BibRef

Tang, K.H.[Kai-Hua], Niu, Y.L.[Yu-Lei], Huang, J.Q.[Jian-Qiang], Shi, J.X.[Jia-Xin], Zhang, H.W.[Han-Wang],
Unbiased Scene Graph Generation From Biased Training,
CVPR20(3713-3722)
IEEE DOI 2008
Visualization, Training, Task analysis, Predictive models, Dogs, Cognition, Genomics BibRef

Birdal, T.[Tolga], Arbel, M.[Michael], Simsekli, U.[Umut], Guibas, L.J.[Leonidas J.],
Synchronizing Probability Measures on Rotations via Optimal Transport,
CVPR20(1566-1576)
IEEE DOI 2008
Synchronization, Quaternions, Atmospheric measurements, Particle measurements, Computer vision, Rotation measurement, Manifolds BibRef

Raboh, M.[Moshiko], Herzig, R.[Roei], Berant, J.[Jonathan], Chechik, G.[Gal], Globerson, A.[Amir],
Differentiable Scene Graphs,
WACV20(1477-1486)
IEEE DOI 2006
Task analysis, Cognition, Visualization, Feature extraction, Proposals, Image edge detection, Training BibRef

Chen, L., Zhang, H., Xiao, J., He, X., Pu, S., Chang, S.,
Counterfactual Critic Multi-Agent Training for Scene Graph Generation,
ICCV19(4612-4622)
IEEE DOI 2004
biology computing, entropy, genomics, gradient methods, graph theory, image processing, learning (artificial intelligence), BibRef

Liu, S.F.[Si-Fei], Li, X.T.[Xue-Ting], Jampani, V.[Varun], Mello, S.[Shalini], Kautz, J.[Jan],
Learning Propagation for Arbitrarily-Structured Data,
ICCV19(652-661)
IEEE DOI 2004
superpixels, point clouds. graph theory, image segmentation, learning (artificial intelligence), object detection, CNN, SGPN, Convolution BibRef

Chen, T.S.[Tian-Shui], Xu, M.X.[Mu-Xin], Hui, X.L.[Xiao-Lu], Wu, H.F.[He-Feng], Lin, L.A.[Li-Ang],
Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition,
ICCV19(522-531)
IEEE DOI 2004
Code, Graph Representation.
WWW Link. graph theory, image recognition, learning (artificial intelligence), semantic regions, Task analysis BibRef

Suhail, M., Sigal, L.,
Mixture-Kernel Graph Attention Network for Situation Recognition,
ICCV19(10362-10371)
IEEE DOI 2004
graph theory, image motion analysis, image representation, inference mechanisms, learning (artificial intelligence), Training BibRef

Schroeder, B., Tripathi, S., Tang, H.,
Triplet-Aware Scene Graph Embeddings,
SGRL19(1783-1787)
IEEE DOI 2004
data visualisation, graph theory, triplet supervision, data augmentation, scene graph representation, visualization, graph neural network BibRef

Gkanatsios, N., Pitsikalis, V., Koutras, P., Maragos, P.,
Attention-Translation-Relation Network for Scalable Scene Graph Generation,
SGRL19(1754-1764)
IEEE DOI 2004
computer graphics, feature extraction, attention-translation-relation network, background class, Vision and Language BibRef

Li, Y.M.[Yi-Ming], Yang, X.S.[Xiao-Shan], Xu, C.S.[Chang-Sheng],
Structured Neural Motifs: Scene Graph Parsing via Enhanced Context,
MMMod20(II:175-188).
Springer DOI 2003
BibRef

Chen, T.S.[Tian-Shui], Yu, W.H.[Wei-Hao], Chen, R.Q.[Ri-Quan], Lin, L.[Liang],
Knowledge-Embedded Routing Network for Scene Graph Generation,
CVPR19(6156-6164).
IEEE DOI 2002
BibRef

Zhang, J.[Ji], Shih, K.J.[Kevin J.], Elgammal, A.[Ahmed], Tao, A.[Andrew], Catanzaro, B.[Bryan],
Graphical Contrastive Losses for Scene Graph Parsing,
CVPR19(11527-11535).
IEEE DOI 2002
BibRef

Kim, S.[Seungryong], Min, D.B.[Dong-Bo], Jeong, S.[Somi], Kim, S.[Sunok], Jeon, S.[Sangryul], Sohn, K.H.[Kwang-Hoon],
Semantic Attribute Matching Networks,
CVPR19(12331-12340).
IEEE DOI 2002
BibRef

Gu, J.X.[Jiu-Xiang], Zhao, H.D.[Han-Dong], Lin, Z.[Zhe], Li, S.[Sheng], Cai, J.F.[Jian-Fei], Ling, M.Y.[Ming-Yang],
Scene Graph Generation With External Knowledge and Image Reconstruction,
CVPR19(1969-1978).
IEEE DOI 2002
BibRef

Matejek, B.[Brian], Haehn, D.[Daniel], Zhu, H.[Haidong], Wei, D.[Donglai], Parag, T.[Toufiq], Pfister, H.[Hanspeter],
Biologically-Constrained Graphs for Global Connectomics Reconstruction,
CVPR19(2084-2093).
IEEE DOI 2002
BibRef

González, V., Ortega, A.,
Multi-Resolution Spectral Graph Matching,
ICIP19(2319-2323)
IEEE DOI 1910
Graph matching, spectral graph theory, Graph Signal Processing, multi-resolution systems BibRef

Khademi, M.[Mahmoud], Schulte, O.[Oliver],
Dynamic Gated Graph Neural Networks for Scene Graph Generation,
ACCV18(VI:669-685).
Springer DOI 1906
BibRef

Yang, S.[Shuang], Yang, B.[Bo],
Enhanced Network Embedding with Text Information,
ICPR18(326-331)
IEEE DOI 1812
Task analysis, Matrix decomposition, Linear programming, Pattern recognition, Computer science, Twitter BibRef

Douze, M.[Matthijs], Sablayrolles, A.[Alexandre], Jégou, H.[Hervé],
Link and Code: Fast Indexing with Graphs and Compact Regression Codes,
CVPR18(3646-3654)
IEEE DOI 1812
BibRef

Zellers, R.[Rowan], Yatskar, M.[Mark], Thomson, S.[Sam], Choi, Y.J.[Ye-Jin],
Neural Motifs: Scene Graph Parsing with Global Context,
CVPR18(5831-5840)
IEEE DOI 1812
Visualization, Head, Genomics, Bioinformatics, Semantics, Image edge detection, Wheels BibRef

Kurzejamski, G.[Grzegorz], Iwanowski, M.[Marcin],
Selective and Simple Graph Structures for Better Description of Local Point-Based Image Features,
ICCVG18(125-136).
Springer DOI 1810
BibRef

Yang, J.W.[Jian-Wei], Lu, J.[Jiasen], Lee, S.[Stefan], Batra, D.[Dhruv], Parikh, D.[Devi],
Graph R-CNN for Scene Graph Generation,
ECCV18(I: 690-706).
Springer DOI 1810
BibRef

Rayar, F.[Frédéric], Uchida, S.[Seiichi],
An Image-Based Representation for Graph Classification,
SSSPR18(140-149).
Springer DOI 1810
BibRef

Zhou, X.[Xiong], Wang, H.Z.[Han-Zi], Xiao, G.B.[Guo-Bao], Wang, X.[Xing], Yan, Y.[Yan], Zhang, L.M.[Li-Ming],
Weighted median-shift on graphs for geometric model fitting,
ICIP17(555-559)
IEEE DOI 1803
Adaptation models, Computational modeling, Data models, Estimation, Kernel, Standards, Weight measurement, geometric model fitting, weighted median-shift BibRef

Xu, D., Zhu, Y., Choy, C.B., Fei-Fei, L.[Li],
Scene Graph Generation by Iterative Message Passing,
CVPR17(3097-3106)
IEEE DOI 1711
Image edge detection, Message passing, Predictive models, Proposals, Semantics, Visualization BibRef

Drees, D.[Dominik], Scherzinger, A.[Aaron], Jiang, X.Y.[Xiao-Yi],
GERoMe: A Novel Graph Extraction Robustness Measure,
GbRPR17(73-82).
Springer DOI 1706
Graph structure from vector data. BibRef

Schiavinato, M.[Michele], Torsello, A.[Andrea],
Synchronization Over the Birkhoff Polytope for Multi-graph Matching,
GbRPR17(266-275).
Springer DOI 1706
BibRef

de Ita Luna, G.[Guillermo], Marcial-Romero, J.R.[J. Raymundo], Hernández, J.A., Valdovinos, R.M.[Rosa Maria], Romero, M.[Marcelo],
Extending Extremal Polygonal Arrays for the Merrifield-Simmons Index,
MCPR17(22-31).
Springer DOI 1706
Intrinsic properties of molecular graphs. BibRef

Miyazaki, T., Omachi, S.,
Graph model boosting for structural data recognition,
ICPR16(1707-1712)
IEEE DOI 1705
Approximation algorithms, Boosting, Data models, Pattern recognition, Probability, Probability density function, Training, data BibRef

Som, A.[Anirudh], Anirudh, R.[Rushil], Wang, Q.[Qiao], Turaga, P.K.[Pavan K.],
Riemannian Geometric Approaches for Measuring Movement Quality,
DIFF-CV16(1005-1005)
IEEE DOI 1612
BibRef

Anirudh, R., Venkataraman, V., Ramamurthy, K.N., Turaga, P.K.,
A Riemannian Framework for Statistical Analysis of Topological Persistence Diagrams,
DIFF-CV16(1023-1031)
IEEE DOI 1612
BibRef

Bai, S.[Song], Sun, S.Y.[Shao-Yan], Bai, X.[Xiang], Zhang, Z.X.[Zhao-Xiang], Tian, Q.[Qi],
Smooth Neighborhood Structure Mining on Multiple Affinity Graphs with Applications to Context-Sensitive Similarity,
ECCV16(II: 592-608).
Springer DOI 1611
Diffusion process on graph. BibRef

Yue, H.[Han], Zhu, X.Y.[Xin-Yan], Chen, D.[Di], Liu, L.J.[Ling-Jia],
A Multi-scale Settlement Matching Algorithm Based on ARG,
ISPRS16(B2: 139-143).
DOI Link 1610
Attributed Relational Graph. BibRef

Bristow, H.[Hilton], Valmadre, J.[Jack], Lucey, S.[Simon],
Dense Semantic Correspondence Where Every Pixel is a Classifier,
ICCV15(4024-4031)
IEEE DOI 1602
similar high-level structures. Detectors BibRef

Doersch, C.[Carl], Gupta, A.[Abhinav], Efros, A.A.[Alexei A.], Doersch, C., Gupta, A., Efros, A.A.,
Unsupervised Visual Representation Learning by Context Prediction,
ICCV15(1422-1430)
IEEE DOI 1602
Context. Train to predict first patch relative to second. Find similarities. BibRef

Wong, A.[Alex], Yuille, A.L.,
One Shot Learning via Compositions of Meaningful Patches,
ICCV15(1197-1205)
IEEE DOI 1602
Dictionary of image patches represent components of an object. BibRef

Castaldo, F.[Francesco], Zamir, A., Angst, R., Palmieri, F.A.N.[Francesco A.N.], Savarese, S.,
Semantic Cross-View Matching,
SatStreet15(1044-1052)
IEEE DOI 1602
Cameras. Matching the descriptors rather than image values. BibRef

Li, W.[Wei], Wang, C.H.[Chang-Hu], Zhang, L.[Lei], Rui, Y.[Yong], Zhang, B.[Bo],
Scalable Visual Instance Mining with Instance Graph,
BMVC15(xx-yy).
DOI Link 1601
automatically discover frequently appearing visual instances. BibRef

Xu, L.X.[Li-Xiang], Xie, J.[Jin], Wang, X.F.[Xiao-Feng], Luo, B.[Bin],
A Mixed Weisfeiler-Lehman Graph Kernel,
GbRPR15(242-251).
Springer DOI 1511
BibRef

Essa, E.[Ehab], Xie, X.H.[Xiang-Hua], Jones, J.L.[Jonathan-Lee],
Graph Based Lymphatic Vessel Wall Localisation and Tracking,
GbRPR15(345-354).
Springer DOI 1511
BibRef

Nematollahi, M.[Maryam], Zhang, X.P.[Xiao-Ping],
A new robust context-based dense CRF model for image labeling,
ICIP14(5876-5880)
IEEE DOI 1502
Computational modeling BibRef

Dornaika, F.[Fadi], Bosaghzadeh, A.[Alireza], Salmane, H.[Houssam], Ruichek, Y.[Yassine],
Locality Constrained Encoding Graph Construction and Application to Outdoor Object Classification,
ICPR14(2483-2488)
IEEE DOI 1412
Databases BibRef

Paragios, N.[Nikos], Komodakis, N.[Nikos],
Discrete Visual Perception,
ICPR14(18-25)
IEEE DOI 1412
Biological system modeling BibRef

Pinheiro, M.A.[Miguel Amável], Kybic, J.[Jan],
Path Descriptors for Geometric Graph Matching and Registration,
ICIAR14(I: 3-11).
Springer DOI 1410
BibRef

Cho, M.S.[Min-Su], Sun, J.[Jian], Duchenne, O.[Olivier], Ponce, J.[Jean],
Finding Matches in a Haystack: A Max-Pooling Strategy for Graph Matching in the Presence of Outliers,
CVPR14(2091-2098)
IEEE DOI 1409
feature correspondence; graph matching; max pooling; outlier rejection BibRef

Hu, N.[Nan], Rustamov, R.M.[Raif M.], Guibas, L.J.[Leonidas J.],
Stable and Informative Spectral Signatures for Graph Matching,
CVPR14(2313-2320)
IEEE DOI 1409
BibRef
Earlier:
Graph Matching with Anchor Nodes: A Learning Approach,
CVPR13(2906-2913)
IEEE DOI 1309
BibRef

Skoura, A.[Angeliki], Nuzhnaya, T.[Tatyana], Bakic, P.R.[Predrag R.], Megalooikonomou, V.[Vasilis],
Detecting and Localizing Tree Nodes in Anatomic Structures of Branching Topology,
ICIAR13(485-493).
Springer DOI 1307
BibRef

Chen, H.[Hao], Sun, J.[Jitao],
Model construction of mix-valued logical network via observed data,
ICARCV12(413-417).
IEEE DOI 1304
BibRef

Chen, L.[Lifei], Wang, S.R.[Sheng-Rui], Yan, X.H.[Xuan-Hui],
Centroid-based clustering for graph datasets,
ICPR12(2144-2147).
WWW Link. 1302
BibRef

Albarelli, A., Bergamasco, F., Rossi, L., Vascon, S., Torsello, A.,
A stable graph-based representation for object recognition through high-order matching,
ICPR12(3341-3344).
WWW Link. 1302
BibRef

Rubio, J.C.[Jose C.], Serrat, J.[Joan], Lopez, A.[Antonio], Paragios, N.[Nikos],
Image contextual representation and matching through hierarchies and higher order graphs,
ICPR12(2664-2667).
WWW Link. 1302
BibRef

Su, J.[Jiang], Dong, L.[Le], Ren, P.[Peng], Hancock, E.R.[Edwin R.],
Hypergraph matching based on Marginalized Constrained Compatibility,
ICPR12(2922-2925).
WWW Link. 1302
BibRef

Donoser, M.[Michael], Urschler, M.[Martin], Bischof, H.[Horst],
Learning Edge-Specific Kernel Functions For Pairwise Graph Matching,
BMVC12(17).
DOI Link 1301
BibRef

Gould, S.[Stephen], Zhang, Y.H.[Yu-Hang],
PatchMatchGraph: Building a Graph of Dense Patch Correspondences for Label Transfer,
ECCV12(V: 439-452).
Springer DOI 1210
BibRef

Srinivas, U.[Umamahesh], Monga, V.[Vishal], Raj, R.G.[Raghu G.],
Automatic target recognition using discriminative graphical models,
ICIP11(33-36).
IEEE DOI 1201
BibRef

Wang, D.H.[Dong-Hui], Deng, X.[Xiao],
Learning structural conjunction of image content by sparse graphical model,
ICIP11(45-48).
IEEE DOI 1201
BibRef

Wang, H.[Hua], Nie, F.P.[Fei-Ping], Huang, H.[Heng], Ding, C.[Chris],
Heterogeneous Visual Features Fusion via Sparse Multimodal Machine,
CVPR13(3097-3102)
IEEE DOI 1309
Data Integration; Structured Sparsity; Visual Features Fusion BibRef

Wang, H.[Hua], Nie, F.P.[Fei-Ping], Cai, W.D.[Wei-Dong], Huang, H.[Heng],
Semi-supervised Robust Dictionary Learning via Efficient l-Norms Minimization,
ICCV13(1145-1152)
IEEE DOI 1403
Dictionary Learning BibRef

Nie, F.P.[Fei-Ping], Wang, H.[Hua], Huang, H.[Heng], Ding, C.[Chris],
Unsupervised and semi-supervised learning via l1-norm graph,
ICCV11(2268-2273).
IEEE DOI 1201
BibRef

Wang, H.[Hua], Huang, H.[Heng], Ding, C.[Chris],
Multi-label Feature Transform for Image Classifications,
ECCV10(IV: 793-806).
Springer DOI 1009
For dealing with annotation problems. multi-label, multi-class. BibRef

Wang, H.[Hua], Ding, C.[Chris], Huang, H.[Heng],
Multi-label Linear Discriminant Analysis,
ECCV10(VI: 126-139).
Springer DOI 1009
BibRef

Strug, B.[Barbara],
Using Kernels on Hierarchical Graphs in Automatic Classification of Designs,
GbRPR11(335-344).
Springer DOI 1105
Evaluate designs in CAD system. BibRef

Wang, A.[Aiping], Li, S.[Sikun], Zeng, L.[Liang],
Multiple Order Graph Matching,
ACCV10(III: 471-482).
Springer DOI 1011
BibRef

Su, B.Y.[Bor-Yiing], Brutch, T.G.[Tasneem G.], Keutzer, K.[Kurt],
A parallel region based object recognition system,
WACV11(81-88).
IEEE DOI 1101
BibRef
Earlier:
Parallel BFS graph traversal on images using structured grid,
ICIP10(4489-4492).
IEEE DOI 1009
Breadth first search. BibRef

Wang, S.J.[Shi-Jun], Petrick, N.[Nicholas], van Uitert, R.L.[Robert L.], Periaswamy, S.[Senthil], Summers, R.M.[Ronald M.],
Graph matching based on mean field theory,
ICIP10(1893-1896).
IEEE DOI 1009
BibRef

Zass, R.[Ron], Shashua, A.[Amnon],
Probabilistic graph and hypergraph matching,
CVPR08(1-8).
IEEE DOI 0806

See also Multi-way Clustering Using Super-Symmetric Non-negative Tensor Factorization. BibRef

Zass, R.[Ron], Shashua, A.[Amnon],
Nonnegative Sparse PCA,
NIPS07(xx-yy). BibRef 0700

Zass, R.[Ron], Shashua, A.[Amnon],
A Unifying Approach to Hard and Probabilistic Clustering,
ICCV05(I: 294-301).
IEEE DOI 0510
Analysis of clustering approaches. BibRef

Fan, N.[Na],
Feature-Based Partially Occluded Object Recognition,
ICPR10(3001-3004).
IEEE DOI 1008
Geometry, color, texture BibRef

Revaud, J.[Jerome], Lavoué, G.[Guillaume], Ariki, Y.[Yasuo], Baskurt, A.[Atilla],
Learning an Efficient and Robust Graph Matching Procedure for Specific Object Recognition,
ICPR10(754-757).
IEEE DOI 1008
BibRef
And:
Scale-invariant proximity graph for fast probabilistic object recognition,
CIVR10(414-421).
DOI Link 1007
BibRef
Earlier:
Fast and cheap object recognition by linear combination of views,
CIVR07(194-201).
DOI Link 0707
BibRef

Gu, S.[Steve], Tomasi, C.[Carlo],
Branch and track,
CVPR11(1169-1174).
IEEE DOI 1106
BibRef

Gu, S.[Steve], Zheng, Y.[Ying], Tomasi, C.[Carlo],
Twisted window search for efficient shape localization,
CVPR12(167-173).
IEEE DOI 1208
BibRef
And:
Critical Nets and Beta-Stable Features for Image Matching,
ECCV10(III: 663-676).
Springer DOI 1009
Combine Bag of features and constellation descriptors. BibRef

Croonen, G.[Gerardus], Beleznai, C.[Csaba],
Detection of Near-Regular Object Configurations by Elastic Graph Search,
ICCVG10(I: 283-291).
Springer DOI 1009
Configurations with substantial clutter. BibRef

Arun, R., Suresh, V., Madhavan, C.E.V.[C.E. Veni],
Clustering in Concept Association Networks,
PReMI09(86-91).
Springer DOI 0912
BibRef

Li, X.[Xi], Hu, W.M.[Wei-Ming], Zhang, Z.F.[Zhong-Fei], Liu, Y.[Yang],
Spectral Graph Partitioning Based on a Random Walk Diffusion Similarity Measure,
ACCV09(II: 667-676).
Springer DOI 0909
BibRef

Morrison, P.[Paul], Zou, J.J.[Ju Jia],
Efficient hierarchical graph matching,
ICIP09(445-448).
IEEE DOI 0911
BibRef

Shi, D.C.[Dong-Cheng], Yan, G.Q.[Guo-Qing],
Maximally Stable Color Regions Based Natural Scene Recognition,
CISP09(1-5).
IEEE DOI 0910
BibRef

Prankl, J.[Johann], Zillich, M.[Michael], Leibe, B.[Bastian], Vincze, M.[Markus],
Incremental Model Selection for Detection and Tracking of Planar Surfaces,
BMVC10(xx-yy).
HTML Version. 1009
BibRef

Prankl, J.[Johann], Antenreiter, M.[Martin], Auer, P.[Peter], Vincze, M.[Markus],
Consistent Interpretation of Image Sequences to Improve Object Models on the Fly,
CVS09(384-393).
Springer DOI 0910
Track multiple objects with occlusions. Build graph based description. Add and remove interest points via reasoning. BibRef

Vasconcelos, C.N.[Cristina Nader], Rosenhahn, B.[Bodo],
Bipartite Graph Matching Computation on GPU,
EMMCVPR09(42-55).
Springer DOI 0908
BibRef

Kunegis, J.[Jerome], Lommatzsch, A.[Andreas], Bauckhage, C.[Christian],
Alternative similarity functions for graph kernels,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Jalali, M.[Mehrdad], Mustapha, N.[Norwati], Mamat, A.[Ali], Sulaiman, M.N.B.[M. Nasir B.],
A new clustering approach based on graph partitioning for navigation patterns mining,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Lee, W.J.[Wan-Jui], Duin, R.P.W.[Robert P. W.],
An Inexact Graph Comparison Approach in Joint Eigenspace,
SSPR08(35-44).
Springer DOI 0812
BibRef

Shin, D.J.[Dong-Joe], Tjahjadi, T.[Tardi],
Similarity Invariant Delaunay Graph Matching,
SSPR08(25-34).
Springer DOI 0812
BibRef

Pohl, M.[Mathias], Birke, P.[Peter],
Interactive Exploration of Large Dynamic Networks,
Visual08(xx-yy).
Springer DOI 0809
BibRef

George, B.[Betsy], Shekhar, S.[Shashi],
Modeling Spatio-temporal Network Computations: A Summary of Results,
GS07(177-194).
Springer DOI 0711
BibRef

Fazl-Ersi, E.[Ehsan], Zelek, J.S.[John S.],
Region detection and description for Object Category Recognition,
CRV07(321-328).
IEEE DOI 0705
BibRef

Fazl-Ersi, E.[Ehsan], Zelek, J.S.[John S.],
Local Graph Matching for Object Category Recognition,
CRV07(73-80).
IEEE DOI 0705
BibRef

Declercq, A.[Arnaud], Piater, J.H.[Justus H.],
Affine Warp Propagation for Fast Simultaneous Modelling and Tracking of Articulated Objects,
ACCV10(III: 422-435).
Springer DOI 1011
BibRef

Declercq, A.[Arnaud], Piater, J.H.[Justus H.],
On-line Simultaneous Learning and Tracking of Visual Feature Graphs,
Learning07(1-6).
IEEE DOI 0706
BibRef

Gokalp, D.[Demir], Aksoy, S.[Selim],
Scene Classification Using Bag-of-Regions Representations,
BP07(1-8).
IEEE DOI 0706
BibRef

Lu, H., Ghanbari, M., Woods, J.,
Establishing Object Correspondences by Utilizing Surrounding Information,
ICIP06(1813-1816).
IEEE DOI 0610
BibRef

Xu, Z.J.[Zi-Jian], Luo, J.B.[Jie-Bo],
Face Recognition by Expression-Driven Sketch Graph Matching,
ICPR06(III: 1119-1122).
IEEE DOI 0609
BibRef

Pawan Kumar, M., Torr, P.H.S., Zisserman, A.,
Extending Pictorial Structures for Object Recognition,
BMVC04(xx-yy).
HTML Version. 0508
Deformable object recognition. Graph model, boundary and texture. BibRef

Bart, E.[Evgeniy], Ullman, S.[Shimon],
Cross-Generalization: Learning Novel Classes from a Single Example by Feature Replacement,
CVPR05(I: 672-679).
IEEE DOI 0507
BibRef
And:
Single-example learning of novel classes using representation by similarity,
BMVC05(xx-yy).
HTML Version. 0509
Add a new class. find the distinguising features. BibRef

Jiang, H.[Hui], Ngo, C.W.[Chong-Wah],
Graph based image matching,
ICPR04(III: 658-661).
IEEE DOI 0409
BibRef

Lee, R.L., Marrs, A., Webb, A., Webber, H.,
Using graphs for statistical object models,
ICIP03(I: 273-276).
IEEE DOI 0312
BibRef

Isard, M.,
PAMPAS: Real-Valued Graphical Models for Computer Vision,
CVPR03(I: 613-620).
IEEE DOI 0307
BibRef

El Badawy, O., Kamel, M.,
Shape retrieval using concavity trees,
ICPR04(III: 111-114).
IEEE DOI 0409
BibRef

El Badawy, O., Kamel, M.,
Shape representation using concavity graphs,
ICPR02(III: 461-464).
IEEE DOI 0211
BibRef

Peura, M.[Markus],
Flexible Heuristic Matching of Attribute Trees,
SCIA01(O-Th1). 0206
BibRef
And: (Listed Twice?) SCIA01(O-Tu4A). 0206
BibRef
Earlier:
Attribute Trees In Image Analysis: Heuristic Matching and Learning Techniques,
CIAP99(1160-1165).
IEEE DOI 9909
BibRef

Peura, M., Visa, A., Kostamo, P.,
A New Approach to Land-Based Cloud Classification,
ICPR96(IV: 143-147).
IEEE DOI 9608
(Helsinki Univ. of Technology, SF) BibRef

Massey, S.[Simon], Jones, G.A.[Graeme A.],
Decomposition and Hierarchy: Efficient Structural Matching of Large Multi-scale Representations,
ScaleSpace99(495-500). BibRef 9900

Sainath, S., Sarkar, S.,
An approximate algorithm for structural matching of images,
ICIP98(I: 798-802).
IEEE DOI 9810
BibRef

Watanabe, Y., Takahashi, K.,
A fast structural matching and its application to pattern analysis of 2-D electrophoresis images,
ICIP98(III: 804-808).
IEEE DOI 9810
BibRef

Wu, A.Y.[Angela Y.], Rosenfeld, A.[Azriel],
Geodesic Visibility in Graphs,
UMD--TR3800, May 1997.
WWW Link.
WWW Link. BibRef 9705

Umeki, H., Mizutani, H.,
Dynamic Link Matching for Multiple Object Recognition,
ICPR96(IV: 65-69).
IEEE DOI 9608
(Real World Computing Partners, J) BibRef

Pulli, K., Shapiro, L.G.,
Triplet-Based Object Recognition Using Synthetic and Real Probability Models,
ICPR96(IV: 75-79).
IEEE DOI 9608
(Univ. of Washington, USA) BibRef

Kinoshita, H.,
An image digital signature system with ZKIP for the graph isomorphism,
ICIP96(III: 247-250).
IEEE DOI 9610
BibRef

Wang, J.T.L., Zhang, K., Chirn, G.W.,
The Approximate Graph Matching Problem,
ICPR94(B:284-288).
IEEE DOI BibRef 9400

Wang, C.H.[Cai-Hua], Abe, K.[Keiichi],
Region Correspondence by Inexact Attributed Planar Graph Matching,
ICCV95(440-447).
IEEE DOI Says more than it does. BibRef 9500

Wang, C.H.[Cai-Hua], Abe, K.[Keiichi],
Region Correspondence for Color Scene Images Taken from Different Viewpoints,
MVA94(26-29). Geometric relations only. BibRef 9400

Pik, J.,
Structural patterns or discrete events? A link between pattern recognition and discrete-event systems,
ICPR92(II:290-293).
IEEE DOI 9208
BibRef

Gaudron, I.,
2D objects recognition by graph matching,
ICPR92(II:508-511).
IEEE DOI 9208
BibRef

Esposito, F., Malerba, D., Semeraro, G.,
Flexible Matching for Noisy Structural Descriptions,
IJCAI91(658-664). BibRef 9100

Wang, X.J.[Xiao-Jun], Fu, J.[Jie], Wu, L.D.[Li-De],
A matching algorithm based on hierarchical primitive structure,
ICPR90(I: 285-287).
IEEE DOI 9006
BibRef

Yang, H.[Hefei], Tai, J.W.[Ju-Wei],
On isomorphisms of attributed relational graphs for pattern analysis and a new branch and bound algorithm,
ICPR88(II: 957-959).
IEEE DOI 8811
BibRef

Granger, C.,
Symbolic Scene Matching,
ICPR84(883-885). BibRef 8400

Khan, N.A., Jain, R.,
Matching an Imprecise Object Description with Models in a Knowledge Base,
ICPR84(1131-1134). BibRef 8400

Diamond, M.D., Narasimhamurthi, N., and Ganapathy, S.,
A Systematic Approach to Continuous Graph Labeling with Application to Computer Vision,
AAAI-82(50-54). BibRef 8200

Tanimoto, S.L., and Pavlidis, T.,
Graph Labelling Algorithms for Picture Analysis,
ICPR76(749-752). BibRef 7600

Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Social Networks, Creation, Visualization, Use .


Last update:Jul 28, 2021 at 22:23:09