13.3.11 Matching Using "Tree" Searching Techniques, Heuristic Search

Slagle, J.R., Lee, R.C.T.,
Applications of Game Tree Searching Techniques to Sequential Pattern Recognition,
CACM(14), 1971, pp. 103-110. BibRef 7100

Haralick, R.M., and Shapiro, L.G.,
The Consistent Labeling Problem: Part I,
PAMI(1), No. 2, April 1979, pp. 173-184. BibRef 7904
And:
The Consistent Labeling Problem: Part II,
PAMI(2), No. 3, May 1980, pp. 193-203. BibRef
Earlier:
The Consistent Labeling Problem and Some Applications to Scene Analysis,
ICPR78(616-619). BibRef
And:
The Consistent Labeling Problem,
PRAI-78(173-178). Explore how the problem is done and various operators that can make it faster. BibRef

Shapiro, L.G., and Haralick, R.M.,
Structural Descriptions and Inexact Matching,
PAMI(3), No. 5, September 1981, pp. 504-519. BibRef 8109
Earlier:
Algorithms for Inexact Matching,
ICPR80(202-207). Relaxation, Evaluation. Use of Null nodes. This paper discusses structural description methods (using parts and interrelationships of the parts), and matching techniques based on tree searching (backtrack alone, forwardchecking, and looking ahead). Two kind of matching are described: exact where every relation matches and inexact that is not perfect, only good enough (a mapping such that the weighted sum of the corresponding relations is greater than some given threshold, and the weighted sum of non-matching elements is less than a threshold). Finding the best match is more complex: how do you compare 2 matches when there are good and bad points to each? Searching eliminates impossible (unlikely) paths by considering not only the error in the matches found so far but the minimum error that can occur in the future assignments as constrained by the past labels. Forward checking looks at all future labels, looking ahead only considers the next set of assignments. A look ahead by two assignments is the same as discrete relaxation. The forward checking produces the best results mostly because of the extra computation of the lookahead operations. When more errors are introduced the problem becomes much harder. A major conclusion of the paper is that the inexact matching (consistent labeling) problem is much harder than the exact matching problem. BibRef

Shapiro, L.G.,
Inexact Matching in ESP3,
ICPR76(759-763). BibRef 7600

Haralick, R.M., Ullmann, J.R., and Shapiro, L.G.,
Computer Architecture for Solving Consistent Labeling Problems,
Computer Journal(28), No. 2, 1985, pp. 105-111. BibRef 8500

Haralick, R.M.[Robert M.], and Elliott, G.L.[Gordon L.],
Increasing Tree Search Efficiency for Constraint Satisfaction Problems,
AI(14), No. 3, October 1980, pp. 263-313.
Elsevier DOI BibRef 8010
Earlier: IJCAI79(356-364). BibRef

Rubin, S.[Steve],
Natural Scene Recognition Using Locus Search,
CGIP(13), No. 4, August 1980, pp. 298-333.
Elsevier DOI BibRef 8008

Rubin, S., and Reddy, R.,
The Locus Model of Search and its Use in Image Interpretation,
IJCAI77(590-595). BibRef 7700
And: DARPA77(12-14). Locus, or beam search applied to vision. BibRef

Rubin, S.[Steve],
The ARGOS Image Understanding System,
Ph.D.Thesis (CS), 1978. BibRef 7800 CMU-CS-TR-Report, CMU CS Dept. BibRef
Earlier: DARPAN78(159-162). Pose Estimation. Color. Viewpoint Constraint. The matching method used in HARPY speech program applied to vision, recognition at the basic region level. It requires a detailed model to specify what is possible. BibRef

Boyer, K.L., Vayda, A.J., and Kak, A.C.,
Robotic Manipulation Experiments Using Structural Stereopsis for 3D Vision,
IEEE_EXPERT(1), Fall 1986, pp. 73-94. This reports on results of the work that is covered in the next paper, but is a less technical vision article. BibRef 8600

Boyer, K.L., and Kak, A.C.,
Structural Stereo for 3-D Vision,
PAMI(10), No. 2, March 1988, pp. 144-166.
IEEE DOI BibRef 8803
Earlier:
Symbolic Stereo from Structural Descriptions,
CAIA85(82-87). There is a lot in the paper, primarily it is a matching method. The comparison technique is described in information theoretic terms, but is basically standard, the difference is a triangle function with a peak for no difference between the two and a limit on where zero is reached. The search method is standard tree search, start with the ones that have the fewest options (get the set of best matches and take them only if they are good enough), also there is a nice NIL mapping technique -- NIL is the match of last resort (i.e. at the end of every path in the search tree) but is added to the possible matches only if no other match is good enough. The system uses an information theoretic distance measure (essentially the probaability that two corresponding elements will have the given difference). BibRef

Vayda, A.J., and Kak, A.C.,
A Robot Vision System for Recognition of Generic Shaped Objects,
CVGIP(54), No. 1, July 1991, pp. 1-46.
Elsevier DOI BibRef 9107
Earlier:
INGEN: A Robot Vision System for Generic Object Recognition,
CADBV91(166-175). A generic object (parallelepipeds and cylinders) recognition system, that extracts object hypotheses, geometric reasoning to find size and detect geometric inconsistencies and recognition to reject hypotheses which have little support. Uses range data. BibRef

van der Helm, P.A., Leeuwenberg, E.L.J.,
Avoiding explosive search in automatic selection of simplest pattern codes,
PR(19), No. 2, 1986, pp. 181-191.
Elsevier DOI 0309
BibRef

Newborn, M.,
Unsynchronized iteratively deepening parallel alpha-beta search,
PAMI(10), No. 5, September 1988, pp. 687-694.
IEEE DOI 0401
BibRef

Schaeffer, J.,
The history heuristic and alpha-beta search enhancements in practice,
PAMI(11), No. 11, November 1989, pp. 1203-1212.
IEEE DOI 0401
BibRef

Powley, C., Korf, R.E.,
Single-agent parallel window search,
PAMI(13), No. 5, May 1991, pp. 466-477.
IEEE DOI 0401
BibRef

Kaindl, H., Shams, R., Horacek, H.,
Minimax search algorithms with and without aspiration windows,
PAMI(13), No. 12, December 1991, pp. 1225-1235.
IEEE DOI 0401
BibRef

Yang, G.Z.[Guang-Zheng],
The search algorithms stimulated by premise set in the syntactic knowledge system,
PR(26), No. 1, January 1993, pp. 17-22.
Elsevier DOI 0401
BibRef

Paglieroni, D.W., Ford, G.E., Tsujimoto, E.M.,
The Position-Orientation Masking Approach To Parametric Search For Template Matching,
PAMI(16), No. 7, July 1994, pp. 740-747.
IEEE DOI BibRef 9407

Reinefeld, A., Marsland, T.A.,
Enhanced Iterative-Deepening Search,
PAMI(16), No. 7, July 1994, pp. 701-710.
IEEE DOI BibRef 9407

Ben-Arie, J., and Meiri, A.Z.,
3D Objects Recognition by Optimal Matching Search of Multinary Relations Graphs,
CVGIP(37), No. 3, March 1987, pp. 345-361.
Elsevier DOI Recognize Three-Dimensional Objects. BibRef 8703
Earlier:
3-D Objects Recognition by State Space Search: Optimal Geometric Matching,
CVPR86(456-461). BibRef
And:
Optimal Recognition of 3-D Objects By Search: Generic Models,
ICPR86(100-103). 3D shape matching, using heuristics to limit the cost of the search. (Ignore the grammar problems in the title.) BibRef

Ben-Arie, J., and Meiri, A.Z.,
Three-Dimensional Object Recognition by Two-Dimensional Inclined Shapes Matching with Area Ratios Method,
Draftfall 1984. (Technion - Israel) The interesting thing is the ratio of the area of the lobe to the whole area. This is the feature used in the comparison. Everything else is straightforward. BibRef 8400

Kuno, Y., Okamoto, Y., Okada, S.,
Robot vision using a feature search strategy generated from a 3D object model,
PAMI(13), No. 10, October 1991, pp. 1085-1097.
IEEE DOI 0401
BibRef
Earlier:
Object Recognition Using a Feature Search Strategy Generated from a 3-D Model,
ICCV90(626-635).
IEEE DOI BibRef

Spirkovska, L.,
Three-Dimensional Object Recognition Using Similar Triangles and Decision Trees,
PR(26), No. 5, May 1993, pp. 727-732.
Elsevier DOI BibRef 9305

Ishida, T.,
Real-Time Bidirectional Search: Coordinated Problem-Solving in Uncertain Situations,
PAMI(18), No. 6, June 1996, pp. 617-628.
IEEE DOI 9607
Search. BibRef

Chaudhury, S., Acharyya, A., Subramanian, S., Parthasarathy, G.,
Recognition of Occluded Objects with Heuristic Search,
PR(23), No. 6, 1990, pp. 617-635.
Elsevier DOI BibRef 9000

Chaudhury, S., Subramanian, S., Parthasarathy, G.,
Recognition of Partial Planar Shapes in Limited Memory Environments,
PRAI(4), 1990, pp. 603-628. BibRef 9000

Ishida, T., Korf, R.E.,
Moving-Target Search: A Real-Time Search for Changing Goals,
PAMI(17), No. 6, June 1995, pp. 609-619.
IEEE DOI BibRef 9506

Cho, C.J., Kim, J.H.,
Recognizing 3-D Objects by Forward Checking Constrained Tree Search,
PRL(13), 1992, pp. 587-597. BibRef 9200

Stewart, B.S., Liaw, C.F., White, III, C.C.,
A Bibliography of Heuristic Search Research Through 1992,
SMC(24), 1994, pp. 268-293. BibRef 9400

Chung, K.L., Wu, J.G., Lan, J.K.,
Efficient Search Algorithm on Compact S-Trees,
PRL(18), No. 14, December 1997, pp. 1427-1434. 9806
BibRef

Cantoni, V., Cinque, L., Guerra, C., Levialdi, S., Lombardi, L.,
2-D Object Recognition by Multiscale Tree Matching,
PR(31), No. 10, October 1998, pp. 1443-1454.
Elsevier DOI 9808
BibRef

Raman, A.[Anand], Andreae, P.[Peter], Patrick, J.[Jon],
A Beam Search Algorithm for PFSA Inference,
PAA(1), No. 2, 1998, pp. 121-129. BibRef 9800

Joseph, S.H.,
Analysing and reducing the cost of exhaustive correspondence search,
IVC(17), No. 11, September 1999, pp. 815-830.
Elsevier DOI BibRef 9909

Pridmort, T.P., Joseph, S.H.,
Integrating visual search with visual memory in a knowledge directed image interpretation system,
BMVC90(xx-yy).
PDF File. 9009
BibRef

Silvela, J., Portillo, J.,
Breadth-first search and its application image processing problems,
IP(10), No. 8, August 2001, pp. 1194-1199.
IEEE DOI 0108
BibRef

Wang, J.K.[Jian-Kang], Li, X.B.[Xiao-Bo],
Controlled accurate searches with balloons,
PR(36), No. 3, March 2003, pp. 827-843.
Elsevier DOI 0301
BibRef

Breuel, T.M.,
On the use of interval arithmetic in geometric branch and bound algorithms,
PRL(24), No. 9-10, June 2003, pp. 1375-1384.
Elsevier DOI 0304
BibRef

Breuel, T.M.[Thomas M.],
A Comparison of Search Strategies for Geometric Branch and Bound Algorithms,
ECCV02(III: 837 ff.).
Springer DOI 0205
BibRef

Breuel, T.M.[Thomas M.],
Implementation techniques for geometric branch-and-bound matching methods,
CVIU(90), No. 3, June 2003, pp. 258-294.
Elsevier DOI 0307
BibRef

Sun, C.M.[Chang-Ming], Pallottino, S.[Stefano],
Circular shortest path in images,
PR(36), No. 3, March 2003, pp. 709-719.
Elsevier DOI 0301
BibRef
Earlier:
Circular Shortest Path on Regular Grids,
ACCV02(852-857). BibRef

Appleton, B.[Ben], Sun, C.M.[Chang-Ming],
Circular shortest paths by branch and bound,
PR(36), No. 11, November 2003, pp. 2513-2520.
Elsevier DOI 0309
BibRef

Sun, C.M.[Chang-Ming], Appleton, B.[Ben],
Multiple Paths Extraction in Images Using a Constrained Expanded Trellis,
PAMI(27), No. 12, December 2005, pp. 1923-1933.
IEEE DOI 0512
Extract multiple paths, rather than a single optimal path. (
See also Finding the Best Set of K Paths through a Trellis with Application to Multitarget Tracking. ) BibRef

Undeger, C., Polat, F.,
Real-Time Edge Follow: A Real-Time Path Search Approach,
SMC-C(37), No. 5, September 2007, pp. 860-872.
IEEE DOI 0710
Real-time path searching. Compared to real-time A*. BibRef

Undeger, C., Polat, F.,
Real-Time Moving Target Evaluation Search,
SMC-C(39), No. 3, May 2009, pp. 366-372.
IEEE DOI 0904
BibRef

Ris, M.[Marcelo], Barrera, J.[Junior], Martins, Jr., D.C.[David C.],
U-curve: A branch-and-bound optimization algorithm for U-shaped cost functions on Boolean lattices applied to the feature selection problem,
PR(43), No. 3, March 2010, pp. 557-568.
Elsevier DOI 1001
Boolean lattice; Branch-and-bound algorithm; U-shaped curve; Feature selection; Subset search; Optimal search BibRef

Reis, M.S.[Marcelo S.], Barrera, J.[Junior],
Solving Problems in Mathematical Morphology through Reductions to the U-Curve Problem,
ISMM13(49-60).
Springer DOI 1305
BibRef

Tsapanos, N.[Nikolaos], Tefas, A.[Anastasios], Pitas, I.[Ioannis],
Online shape learning using binary search trees,
IVC(28), No. 7, July 2010, pp. 1146-1154.
Elsevier DOI 1006
Incremental learning techniques; Online pattern recognition; Binary search trees Binary tree for storage and matching of templates. BibRef

Liu, G.C.[Guang-Can], Lin, Z.C.[Zhou-Chen], Yan, S.C.[Shui-Cheng], Sun, J.[Ju], Yu, Y.[Yong], Ma, Y.[Yi],
Robust Recovery of Subspace Structures by Low-Rank Representation,
PAMI(35), No. 1, January 2013, pp. 171-184.
IEEE DOI 1212
Subspace clustering. BibRef

Liu, G.C.[Guang-Can], Xu, H., Tang, J., Liu, Q., Yan, S.C.[Shui-Cheng],
A Deterministic Analysis for LRR,
PAMI(38), No. 3, March 2016, pp. 417-430.
IEEE DOI 1602
Low-Rank Representation. Coherence. Motion segmentation, saliency, face recognition. BibRef

Zheng, Y.Q.[Yin-Qiang], Liu, G.C.[Guang-Can], Sugimoto, S.[Shigeki], Yan, S.C.[Shui-Cheng], Okutomi, M.[Masatoshi],
Practical low-rank matrix approximation under robust L1-norm,
CVPR12(1410-1417).
IEEE DOI 1208
BibRef

Zheng, Y.Q.[Yin-Qiang], Sugimoto, S.[Shigeki], Okutomi, M.[Masatoshi],
Deterministically maximizing feasible subsystem for robust model fitting with unit norm constraint,
CVPR11(1825-1832).
IEEE DOI 1106
BibRef

Htoo, H.[Htoo], Ohsawa, Y.[Yutaka], Sonehara, N.[Noboru], Sakauchi, M.[Masao],
Incremental Single-Source Multi-Target A* Algorithm for LBS Based on Road Network Distance,
IEICE(E96-D), No. 5, May 2013, pp. 1043-1052.
WWW Link. 1305
Shortest path in road network. BibRef

Perakis, P., Passalis, G., Theoharis, T., Kakadiaris, I.A.,
3D Facial Landmark Detection under Large Yaw and Expression Variations,
PAMI(35), No. 7, 2013, pp. 1552-1564.
IEEE DOI face recognition; 3D facial landmark detection; principal curvature value; spin images; Eigenvalues and eigenfunctions 1307
BibRef

Bazin, J.C., Li, H.D.[Hong-Dong], Kweon, I.S.[In So], Demonceaux, C., Vasseur, P., Ikeuchi, K.,
A Branch-and-Bound Approach to Correspondence and Grouping Problems,
PAMI(35), No. 7, 2013, pp. 1565-1576.
IEEE DOI 1307
object recognition; tree searching BibRef

Antikainen, H.[Harri],
Using the Hierarchical Pathfinding A* Algorithm in GIS to Find Paths through Rasters with Nonuniform Traversal Cost,
IJGI(2), No. 4, 2013, pp. 996-1014.
DOI Link 1310
BibRef

Chen, B.Y., Lam, W.H.K., Li, Q., Sumalee, A., Yan, K.,
Shortest Path Finding Problem in Stochastic Time-Dependent Road Networks With Stochastic First-In-First-Out Property,
ITS(14), No. 4, 2013, pp. 1907-1917.
IEEE DOI 1312
Algorithm design and analysis BibRef

Yoon, S., Shim, D.H.,
SLPA*: Shape-Aware Lifelong Planning A* for Differential Wheeled Vehicles,
ITS(16), No. 2, April 2015, pp. 730-740.
IEEE DOI 1504
Heuristic algorithms BibRef

Wu, F.[Fan], Fu, K.[Kun], Wang, Y.[Yang], Xiao, Z.B.[Zhi-Bin],
A Graph-Based Min-# and Error-Optimal Trajectory Simplification Algorithm and Its Extension towards Online Services,
IJGI(6), No. 1, 2017, pp. xx-yy.
DOI Link 1702
BibRef

Pal, P.S., Kar, R., Mandal, D., Ghoshal, S.P.,
A hybrid backtracking search algorithm with wavelet mutation-based nonlinear system identification of Hammerstein models,
SIViP(11), No. 5, July 2017, pp. 921-928.
WWW Link. 1706
BibRef

Pinheiro, M.A.[Miguel Amavel], Kybic, J.[Jan], Fua, P.[Pascal],
Geometric Graph Matching Using Monte Carlo Tree Search,
PAMI(39), No. 11, November 2017, pp. 2171-2185.
IEEE DOI 1710
BibRef
Earlier: A1, A2, Only:
Geometrical graph matching using Monte Carlo tree search,
ICIP15(3145-3149)
IEEE DOI 1512
Biomedical imaging, Computational modeling, Image edge detection, Monte Carlo methods, Roads, Geometric graph matching, Monte Carlo tree search, curve descriptor, image registration BibRef

Ait Bouziaren, S., Aghezzaf, B.,
An Improved Augmented epsilon-Constraint and Branch-and-Cut Method to Solve the TSP With Profits,
ITS(20), No. 1, January 2019, pp. 195-204.
IEEE DOI 1901
Optimization, Traveling salesman problems, Approximation algorithms, Intelligent transportation systems, e-constraint BibRef

Khoa, N.L.D.[Nguyen Lu Dang], Wang, Y.[Yang], Chawla, S.[Sanjay],
Incremental commute time and its online applications,
PR(88), 2019, pp. 101-112.
Elsevier DOI 1901
Commute time, Random walks, Online learning, Anomaly detection, Manifold learning BibRef

Seo, K.[Kwangwon], Ahn, J.H.[Jin-Hyun], Im, D.H.[Dong-Hyuk],
Optimization of Shortest-Path Search on RDBMS-Based Graphs,
IJGI(8), No. 12, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Dimitrov, M.[Miroslav], Baitcheva, T.[Tsonka], Nikolov, N.[Nikolay],
Efficient Generation of Low Autocorrelation Binary Sequences,
SPLetters(27), 2020, pp. 341-345.
IEEE DOI 2003
Aperiodic autocorrelation function, binary sequences, peak sidelobe level (psl), shotgun hill climbing BibRef

Silva-Gálvez, A., Lara-Cárdenas, E., Amaya, I., Cruz-Duarte, J.M., Ortiz-Bayliss, J.C.,
A Preliminary Study on Score-based Hyper-heuristics for Solving the Bin Packing Problem,
MCPR20(318-327).
Springer DOI 2007
BibRef

Zhang, J.L.[Ji-Lian], Wei, K.M.[Kai-Min], Deng, X.L.[Xue-Lian],
Heuristic algorithms for diversity-aware balanced multi-way number partitioning,
PRL(136), 2020, pp. 56-62.
Elsevier DOI 2008
Artificial intelligence, Number partitioning, Heuristic algorithms, Balanced multi-way number partitioning BibRef

Liu, X.C.[Xing-Chi], Derakhshani, M.[Mahsa], Lambotharan, S.[Sangarapillai], van der Schaar, M.[Mihaela],
Risk-Aware Multi-Armed Bandits With Refined Upper Confidence Bounds,
SPLetters(28), 2021, pp. 269-273.
IEEE DOI 2102
Signal processing algorithms, Indexes, Gaussian distribution, Uncertainty, Random variables, Standards, Measurement uncertainty, exploration and exploitation BibRef

Xu, X.P.[Xiang-Ping], Li, J.[Jun], Zhou, M.C.[Meng-Chu],
Bi-Objective Colored Traveling Salesman Problems,
ITS(23), No. 7, July 2022, pp. 6326-6336.
IEEE DOI 2207
Color, Urban areas, Traveling salesman problems, Search problems, Optimization, Statistics, Sorting, variable neighborhood search BibRef

Wang, H.Y.[Huan-Yu], Qin, Z.Q.[Ze-Qun], Li, S.Y.[Song-Yuan], Li, X.[Xi],
CoDiNet: Path Distribution Modeling With Consistency and Diversity for Dynamic Routing,
PAMI(44), No. 10, October 2022, pp. 6011-6023.
IEEE DOI 2209
Path through network. Routing, Computational modeling, Computational efficiency, Training, Image color analysis, Recurrent neural networks, dynamic routing BibRef

Meng, X.H.[Xiang-Hu], Li, J.[Jun], Dai, X.Z.[Xian-Zhong], Dou, J.P.[Jian-Ping],
Variable Neighborhood Search for a Colored Traveling Salesman Problem,
ITS(19), No. 4, April 2018, pp. 1018-1026.
IEEE DOI 1804
Biological cells, Color, Encoding, Genetic algorithms, Optimization, Traveling salesman problems, Urban areas, variable neighborhood search BibRef

Xu, X.P.[Xiang-Ping], Li, J.[Jun], Zhou, M.C.[Meng-Chu],
Delaunay-Triangulation-Based Variable Neighborhood Search to Solve Large-Scale General Colored Traveling Salesman Problems,
ITS(22), No. 3, March 2021, pp. 1583-1593.
IEEE DOI 2103
Urban areas, Traveling salesman problems, Image color analysis, Color, Intelligent transportation systems, Search problems, intelligent optimization BibRef

Zhou, Y.M.[Yang-Ming], Xu, W.Q.[Wen-Qiang], Fu, Z.H.[Zhang-Hua], Zhou, M.C.[Meng-Chu],
Multi-Neighborhood Simulated Annealing-Based Iterated Local Search for Colored Traveling Salesman Problems,
ITS(23), No. 9, September 2022, pp. 16072-16082.
IEEE DOI 2209
Urban areas, Color, Traveling salesman problems, Simulated annealing, Biological cells, Upper bound, Robots, colored traveling salesman problem BibRef

Fan, H.M.[Hou-Ming], Peng, W.H.[Wen-Hao], Ma, M.Z.[Meng-Zhi], Yue, L.J.[Li-Jun],
Storage Space Allocation and Twin Automated Stacking Cranes Scheduling in Automated Container Terminals,
ITS(23), No. 9, September 2022, pp. 14336-14348.
IEEE DOI 2209
Containers, Resource management, Cranes, Loading, Optimization, Safety, Stacking, Automated container terminal, handshake area, variable neighborhood search based hybrid genetic algorithm BibRef

Fan, A.X.[Ao-Xiang], Ma, J.Y.[Jia-Yi], Jiang, X.Y.[Xing-Yu], Ling, H.B.[Hai-Bin],
Efficient Deterministic Search With Robust Loss Functions for Geometric Model Fitting,
PAMI(44), No. 11, November 2022, pp. 8212-8229.
IEEE DOI 2210
Estimation, Computational modeling, Benchmark testing, Search problems, Approximation algorithms, Analytical models, image matching BibRef

Wang, X.[Xiao], Chen, Z.[Zhe], Jiang, B.[Bo], Tang, J.[Jin], Luo, B.[Bin], Tao, D.C.[Da-Cheng],
Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-Based Beam Search,
IP(31), 2022, pp. 6239-6254.
IEEE DOI 2210
Target tracking, Tracking, Visualization, Search problems, Reinforcement learning, Trajectory, Decision making, greedy search BibRef

Zhang, R.K.[Rong-Kai], Zhang, C.[Cong], Cao, Z.G.[Zhi-Guang], Song, W.[Wen], Tan, P.S.[Puay Siew], Zhang, J.[Jie], Wen, B.[Bihan], Dauwels, J.[Justin],
Learning to Solve Multiple-TSP With Time Window and Rejections via Deep Reinforcement Learning,
ITS(24), No. 1, January 2023, pp. 1325-1336.
IEEE DOI 2301
Task analysis, Costs, Routing, Reinforcement learning, Time factors, Training data, Market research, Travelling salesman problem, deep reinforcement learning BibRef

Wang, K.[Ke], Feng, B.R.[Bao-Rui], Ma, Y.[Ying], Lin, W.L.[Wen-Liang], Zhao, J.G.[Jin-Gui],
List Encoding of Vector Perturbation Precoding,
SPLetters(30), 2023, pp. 478-482.
IEEE DOI 2305
Perturbation methods, Precoding, Optimization, Signal processing algorithms, Search problems, VP BibRef

Ling, Z.X.[Zheng-Xuan], Zhang, Y.[Yu], Chen, X.[Xi],
A Deep Reinforcement Learning Based Real-Time Solution Policy for the Traveling Salesman Problem,
ITS(24), No. 6, June 2023, pp. 5871-5882.
IEEE DOI 2306
Urban areas, Real-time systems, Heuristic algorithms, Reinforcement learning, Neural networks, Training, traveling salesman BibRef

Chole, V.[Vikrant], Gadicha, V.[Vijay],
Locust Mayfly Optimization-Tuned Neural Network for AI-Based Pruning in Chess Game,
IJIG(23), No. 4 2023, pp. 2350028.
DOI Link 2308
BibRef

Kim, G.[Geunu], Jin, H.W.[Hyun-Woo], Kim, M.[Mingi], Jang, K.[Kitae], Jang, I.G.[In Gwun],
Loop-Wise Route Representation and Its Optimization Formulation for Symmetric Traveling Salesman Problems,
ITS(24), No. 9, September 2023, pp. 9490-9500.
IEEE DOI 2310
BibRef

Lee, J.[Jiho], Kim, E.[Eunwoo],
Mitigating Search Interference With Task-Aware Nested Search,
IP(33), 2024, pp. 3102-3114.
IEEE DOI 2405
Task analysis, Interference, Search problems, Computer architecture, Multitasking, Costs, Generators, search interference BibRef

Gutiérrez, O.[Omar], Zamora, E.[Erik], Menchaca, R.[Ricardo],
Graph Representation for Learning the Traveling Salesman Problem,
MCPR21(153-162).
Springer DOI 2108
BibRef

Cai, Z.P.[Zhi-Peng], Chin, T., Koltun, V.,
Consensus Maximization Tree Search Revisited,
ICCV19(1637-1645)
IEEE DOI 2004
Code, Search.
WWW Link. computational complexity, optimisation, tree searching, consensus maximization tree structure, Computational modeling BibRef

Bauer, D.[Dominik], Patten, T.[Timothy], Vincze, M.[Markus],
SporeAgent: Reinforced Scene-level Plausibility for Object Pose Refinement,
WACV22(196-204)
IEEE DOI 2202
BibRef
Earlier:
Monte Carlo Tree Search on Directed Acyclic Graphs for Object Pose Verification,
CVS19(386-396).
Springer DOI 1912
Visualization, Codes, Reinforcement learning, Robustness, Iterative methods, Vision for Robotics BibRef

Behzadi, S., Kolbadinejad, M.,
Introducing a Novel Method to Solve Shortest Path Problems Based On Structure of Network Using Genetic Algorithm,
SMPR19(201-203).
DOI Link 1912
BibRef

Osmanlioglu, Y.[Yusuf], Shokoufandeh, A.[Ali],
Multi-layer Tree Matching Using HSTs,
GbRPR15(198-207).
Springer DOI 1511
BibRef

Lam, M.[Michael], Doppa, J.R.[Janardhan Rao], Todorovic, S.[Sinisa], Dietterich, T.G.[Thomas G.],
HC-search for structured prediction in computer vision,
CVPR15(4923-4932)
IEEE DOI 1510
works for language, try for vision. BibRef

Roy, A.[Anirban], Todorovic, S.[Sinisa],
Scene Labeling Using Beam Search under Mutex Constraints,
CVPR14(1178-1185)
IEEE DOI 1409
Beam Search; Mutex Constraints; Scene Labeling BibRef

Sun, M.[Min], Huang, W.[Wan], Savarese, S.[Silvio],
Find the Best Path: An Efficient and Accurate Classifier for Image Hierarchies,
ICCV13(265-272)
IEEE DOI 1403
branch-and-bound BibRef

Mudaliar, D.N.[Devasenathipathi N.], Modi, N.K.[Nilesh K.],
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ICSIPR13(127-130).
IEEE DOI 1304
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Nguyen, H.T.[Hoang Thanh], Bhanu, B.[Bir],
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Kappes, J.H.[Jörg Hendrik], Beier, T.[Thorsten], Schnörr, C.[Christoph],
MAP-Inference on Large Scale Higher-Order Discrete Graphical Models by Fusion Moves,
GMCV14(469-484).
Springer DOI 1504
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Andres, B.[Bjoern], Kappes, J.H.[Jörg H.], Beier, T.[Thorsten], Köthe, U.[Ullrich], Hamprecht, F.A.[Fred A.],
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ECCV12(VII: 154-166).
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Comparing Image Objects Using Tree-Based Approach,
ICCVG12(702-709).
Springer DOI 1210
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An Observation about Circular Shortest Paths: Dealing with Additional Constraints Using Branch and Bound,
DICTA11(513-517).
IEEE DOI 1205
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Chakrabarti, P.P.[Partha Pratim], Aine, S.[Sandip],
New Approaches to Design and Control of Time Limited Search Algorithms,
PReMI09(1-6).
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Zhu, W.X.[Wei-Xing], Chen, X.Y.[Xian-Yong], Li, X.C.[Xin-Cheng],
A New Search Algorithm Based on Muti-Octagon-Grid,
CISP09(1-5).
IEEE DOI 0910
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Dwyer, T.[Tim], Hurst, N.[Nathan], Merrick, D.[Damian],
A Fast and Simple Heuristic for Metro Map Path Simplification,
ISVC08(II: 22-30).
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Boffy, A., Tsin, Y., Genc, Y.,
Real-Time Feature Matching using Adaptive and Spatially Distributed Classification Trees,
BMVC06(II:529).
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A New Algorithm to Compute the Distance Between Multi-dimensional Histograms,
CIARP07(115-123).
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Serratosa, F.[Francesc], Sanromà, G.[Gerard],
An Efficient Distance Between Multi-dimensional Histograms for Comparing Images,
SSPR06(412-421).
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Serratosa, F.[Francesc], Sanfeliu, A.[Alberto],
Vision-Based Robot Positioning by an Exact Distance Between Histograms,
ICPR06(II: 849-852).
IEEE DOI 0609
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And:
A Fast and Exact Modulo-Distance Between Histograms,
SSPR06(394-402).
Springer DOI 0608
To determine if the image is familiar. BibRef

Serratosa, F.[Francesc], Sanfeliu, A.[Alberto],
Matching Attributed Graphs: 2nd-Order Probabilities for Pruning the Search Tree,
IbPRIA05(II:131).
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Wahl, E.[Eric], Hirzinger, G.[Gerd],
A Method for Fast Search of Variable Regions on Dynamic 3D Point Clouds,
DAGM05(208).
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Thayananthan, A., Stenger, B., Torr, P.H.S., Cipolla, R.,
Learning a Kinematic Prior for Tree-Based Filtering,
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Stenger, B., Thayananthan, A., Torr, P.H.S., Cipolla, R.,
Filtering using a tree-based estimator,
ICCV03(1063-1070).
IEEE DOI 0311
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Huber, D.F.[Daniel F.], Hebert, M.[Martial],
3D Modeling Using a Statistical Sensor Model and Stochastic Search,
CVPR03(I: 858-865).
IEEE DOI
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And: CREST03(125-126). 0309
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Kovtun, I.[Ivan],
Partial Optimal Labeling Search for a NP-Hard Subclass of (max,+) Problems,
DAGM03(402-409).
Springer DOI 0310
Award, GCPR, HM. BibRef

Hao, H.W.[Hong-Wei], Liu, C.L.[Cheng-Lin], Sako, H.,
Comparison of genetic algorithm and sequential search methods for classifier subset selection,
ICDAR03(765-769).
IEEE DOI 0311
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Tabibi, O.D.[Omid David], Netanyahu, N.S.[Nathan S.],
Verified Null-move Pruning,
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WWW Link.
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Jepson, A.D.[Allan D.], Mann, R.[Richard],
Qualitative Probabilities for Image Interpretation,
ICCV99(1123-1130).
IEEE DOI Probabilistic pruning of search tree. BibRef 9900

Greenspan, M.A.[Michael A.],
The Sample Tree: A Sequential Hypothesis Testing Approach to 3D Object Recognition,
CVPR98(772-779).
IEEE DOI BibRef 9800

Chung, H.Y., Cheung, P.Y.S., Yung, N.H.C.,
Adaptive search center non-linear three step search,
ICIP98(II: 191-194).
IEEE DOI 9810
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Commike, A.Y., Hull, J.J.,
Syntactic pattern classification by branch and bound search,
CVPR91(432-437).
IEEE DOI 0403
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Tanimoto, S.L.,
Machine Vision as State-Space Search,
MVAAS88(XX-YY). Search Techniques. Model vision as a search. Describe search techniques. BibRef 8800

Breuel, T.M.[Thomas M.],
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Breuel, T.M.,
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CVPR93(707-708).
IEEE DOI BibRef 9300

Breuel, T.M.,
Fast Recognition Using Adaptive Subdivisions of Transformation Space,
CVPR92(445-451).
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Breuel, T.M.,
Model Based Recognition Using Pruned Correspondence Search,
CVPR91(257-262).
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Breuel, T.M.[Thomas M.],
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Breuel, T.M.[Thomas M.],
Indexing for Visual Recognition from a Large Model Base,
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Breuel, T.M.,
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CVPR88(690-695).
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Brailovsky, V.L.,
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ICPR90(I: 953-956).
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Earlier:
On use of predictive probabilistic estimates for selecting best decision rules in the course of a search,
CVPR88(469-475).
IEEE DOI 0403
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Gennery, D.B.,
A Feature-Based Scene Matcher,
IJCAI81(667-673), (JPL). Match 2 scene descriptions - set of feature vectors, differ by unknown transformation. Method: search by sequentially matching features of one scene to those of the other scene. Computer transformations and probability of match - use these to prune tree. Search: choose of possible match for one element (try all), choose a consistent match for the next element, etc. Standard search problems. Examples are on small numbers. BibRef 8100

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DARPAN79(42-46). Extension of the ARGOS system. BibRef 7900

Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Tabu Search .