4.8 Perceptual Grouping, Perceptual Organization Techniques, Saliency

Chapter Contents (Back)
Grouping, Perceptual. Segmentation, Grouping. Perceptual Grouping.

4.8.1 Perceptual Grouping, Saliency, Theory

Chapter Contents (Back)
Human Vision. Saliency. Non-accidentalness. Grouping, Perceptual. Perceptual Grouping. Perceptual Organization.

Koffka, K.,
Principles of Gestalt Psychology,
New York: Harcourt, Brace and World1935. BibRef 3500 BookGeneral Gestalt psychology book. BibRef

Garner, W.R.,
The Processing of Information and Structure,
Erlbaum1974. BibRef 7400

Kanizsa, G.,
Organization in Vision: Essays on Gestalt Perception,
New York: Praeger1979. The vision reference to grouping and Gestalt psychology. BibRef 7900

Attneave, F.,
Some Informational Aspects of Visual Perception,
PsychR(61), No. 3, 1954, pp. 183-193.
DOI Link Apply Information Theory concepts to perception. Information is concentrated at points of high curvature. BibRef 5400

Attneave, F., and Arnoult, M.D.,
The Quantitative Study of Shape and Pattern Perception,
PsychBul(xx), 1956, pp. 452-471. BibRef 5600

Hochberg, J.E., McAllister, E.,
A Quantitative Approach to Figural 'Goodness',
JEP(46), 1953, pp. 361-364. BibRef 5300

Parker, D.J., Moore, D.J.H.,
End Points, Complexity, and Visual Illusions,
SMC(2), July 1972, pp. 421-429. BibRef 7207

Zahn, C.T.,
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters,
TC(20), No. 1, January 1971, pp. 68-86. Using a graph representation for finding clusters. BibRef 7101

Zobrist, A.L., and Thompson, W.B.,
Building a Distance Function for Gestalt Grouping,
TC(24), No. 7, July 1975, pp. 711-719. BibRef 7507

Zucker, S.W.[Steven W.],
Computational and Psychophysical Experiments in Grouping: Early Orientation Selection,
HMV83(545-567). BibRef 8300

Zucker, S.W.[Steven W.],
Early Orientation Selection: Tangent Fields and the Dimensionality of Their Support,
CVGIP(32), No. 1, October 1985, pp. 74-103.
Elsevier DOI BibRef 8510
And: RCV87(333-347). inference of orientation information in image. BibRef

Zucker, S.W.[Steven W.],
Early Orientation Selection: Inferring Trace, Tangent, and Curvature Fields,
PRAI(2), 1988, pp. 443-457. BibRef 8800
And: ICPR86(294-302). BibRef

Palmer, S.E.,
The Psychology of Perceptual Organization: A Transformational Approach,
HMV83(269-339). Use local invariance over a group of transformations. BibRef 8300

Witkin, A.P., and Tenenbaum, J.M.,
On the Role of Structure in Vision,
HMV83(481-543). BibRef 8300
And:
What is Perceptual Organization for?,
IJCAI83(XX-YY). Non-accidentalness. Regular relationships do not arise from accidents. BibRef

Treisman, A.[Anne],
Preattentive Processing in Vision,
CVGIP(31), No. 2, August 1985, pp. 156-177.
Elsevier DOI BibRef 8508

Leyton, M.[Michael],
Generative Systems Of Analyzers,
CVGIP(31), No. 2, August 1985, pp. 201-241.
Elsevier DOI BibRef 8508

Leyton, M.[Michael],
Perceptual Organization as Nested Control,
BioCyber(51), 1984, pp. 141-153. BibRef 8400

Leyton, M.[Michael],
Nested structures of control: An intuitive view,
CVGIP(37), No. 1, January 1987, pp. 20-53.
Elsevier DOI 0501
BibRef

Triesman, A.,
Perceptual Grouping and Attention in Visual Search for Features and Objects,
JEP:HPP(8), No. 2, 1982, pp. 194-214. BibRef 8200

Pentland, A.P.,
Perceptual Organization and the Representation of Natural Form,
AI(28), No. 3, May 1986, pp. 293-331.
Elsevier DOI Deformable Solids. Parts, descriptions reflect possible formative history of the object. BibRef 8605

Yip, K., and Zhao, F.,
Spatial Aggregation: Theory and Applications,
JAIR(5), 1996, pp. 1-26.
HTML Version. Imagistic reasoning to produce a description of the structure, behavior, or control actions. BibRef 9600

Murino, V., Regazzoni, C.S., Foresti, G.L.,
Grouping as a Searching Process for Minimum-Energy Configurations of Labeled Random-Fields,
CVIU(64), No. 1, July 1996, pp. 157-174.
DOI Link 9608

See also Grouping of Rectilinear Segments by the Labeled Hough Transform. BibRef

Castaño, R.L., Hutchinson, S.A.[Seth A.],
A Probabilistic Approach to Perceptual Grouping,
CVIU(64), No. 3, November 1996, pp. 399-419.
DOI Link 9612
BibRef
Earlier:
A Probabilistic Framework for Grouping Image Features,
SCV95(611-616).
IEEE DOI University of Illinois at Urbana-Champaign. BibRef

Bottoni, P., Cinque, L., Levialdi, S., Mussio, P.,
Matching the Resolution Level to Salient Image Features,
PR(31), No. 1, January 1998, pp. 89-104.
Elsevier DOI 9802
BibRef

Bottoni, P., Cinque, L., Levialdi, S., Lombardi, L., Mussio, P.,
Combining resolution and granularity for pattern recognition,
CIAP95(502-508).
Springer DOI 9509
BibRef

Bottoni, P., Mussio, P., Protti, M.,
Metareasoning as a tool for pattern recognition,
ICPR92(II:285-289).
IEEE DOI 9208
BibRef

Vasseur, P., Pegard, C., Mouaddib, E.M., Delahoche, L.,
Perceptual organization approach based on Dempster-Shafer theory,
PR(32), No. 8, August 1999, pp. 1449-1462.
Elsevier DOI For object detection. BibRef 9908

Roerdink, J.B.T.M.[Jos B.T.M.],
Group morphology,
PR(33), No. 6, June 2000, pp. 877-895.
Elsevier DOI 0004
BibRef
And: Corrigendum: PR(96), 2019, pp. 106978.
Elsevier DOI 1909
BibRef

Roerdink, J.B.T.M.[Jos B.T.M.],
Adaptivity and group invariance in mathematical morphology,
ICIP09(2253-2256).
IEEE DOI 0911
BibRef

Jacobs, D.W.[David W.], Lindenbaum, M.[Michael],
Guest editors' introduction to the special section on perceptual organization in computer vision,
PAMI(25), No. 4, April 2003, pp. 385-386.
IEEE Abstract. 0304
BibRef
And: PAMI(25), No. 6, June 2003, pp. 641-641.
IEEE Abstract. 0306
Two issues. Papers grew out of the POCV workshop (see BibRef PercOrg01) BibRef

Engbers, E.A.[Erik A.], Smeulders, A.W.M.[Arnold W.M.],
Design considerations for generic grouping in vision,
PAMI(25), No. 4, April 2003, pp. 445-457.
IEEE Abstract. 0304
Formal characterization of perceptual grouping. BibRef

Desolneux, A.[Agnes], Moisan, L.[Lionel], Morel, J.M.[Jean-Michel],
A grouping principle and four applications,
PAMI(25), No. 4, April 2003, pp. 508-513.
IEEE Abstract. 0304
Statistical criterion to test whether a cluster is meaningful.
See also Meaningful Alignments.
See also Edge Detection by Helmholtz Principle.
See also Dequantizing image orientation. BibRef

Yantis, S.,
How visual salience wins the battle for awareness,
Nature Neuroscience(8), No. 8, 2005, pp. 975-977. BibRef 0500

Berengolts, A.[Alexander], Lindenbaum, M.[Michael],
On the Distribution of Saliency,
PAMI(28), No. 12, December 2006, pp. 1973-1990.
IEEE DOI 0611
BibRef
Earlier: CVPR04(II: 543-549).
IEEE DOI 0408
BibRef

Lindenbaum, M., Berengolts, A.,
A Probabilistic Interpretation of the Saliency Network,
ECCV00(II: 257-272).
Springer DOI 0003
BibRef

Zlatoff, N.[Nicolas], Tellez, B.[Bruno], Baskurt, A.[Atilla],
Combining local belief from low-level primitives for perceptual grouping,
PR(41), No. 4, April 2008, pp. 1215-1229.
Elsevier DOI 0801
BibRef
Earlier:
Image understanding and scene models: a generic framework integrating domain knowledge and gestalt theory,
ICIP04(IV: 2355-2358).
IEEE DOI 0505
Perceptual grouping, Segmentation, Gestalt laws; Pre-attentive vision, Dempster-Shafer BibRef

Desolneux, A.[Agnès], Moisan, L.[Lionel], Morel, J.M.[Jean-Michel],
From Gestalt Theory to Image Analysis: A Probabilistic Approach,
Springer2008, ISBN: 978-0-387-72635-9.
WWW Link.
See also ZERO: a Local JPEG Grid Origin Detector Based on the Number of DCT Zeros and its Applications in Image Forensics. BibRef 0800

Desolneux, A.[Agnès],
A Probabilistic Grouping Principle to Go from Pixels to Visual Structures,
DGCI11(1-12).
Springer DOI 1104
BibRef

Pal, R., Mukherjee, A., Mitra, P., Mukherjee, J.,
Modelling visual saliency using degree centrality,
IET-CV(4), No. 3, September 2010, pp. 218-229.
DOI Link 1008
BibRef

Rahman, A., Houzet, D., Pellerin, D., Marat, S., Guyader, N.,
Parallel implementation of a spatio-temporal visual saliency model,
RealTimeIP(6), No. 1, March 2011, pp. 3-14.
WWW Link. 1103
BibRef

Seo, H.J.[Hae Jong], Milanfar, P.[Peyman],
Static and Space-Time Visual Saliency Detection by Self-Resemblance,
Vision(9), No. 12, 2009, pp. 1-17.
DOI Link BibRef 0900
Earlier:
Nonparametric bottom-up saliency detection by self-resemblance,
VCL-ViSU09(45-52).
IEEE DOI 0906
Bottom-up detection. BibRef

Ron, E.[Eldar], Spitzer, H.[Hedva],
Is the Kanizsa illusion triggered by the simultaneous contrast mechanism?,
JOSA-A(28), No. 12, December 2011, pp. 2629-2641.
WWW Link. 1112
BibRef

Goferman, S.[Stas], Zelnik-Manor, L.[Lihi], Tal, A.[Ayellet],
Context-Aware Saliency Detection,
PAMI(34), No. 10, October 2012, pp. 1915-1926.
IEEE DOI 1208
BibRef
Earlier: CVPR10(2376-2383).
IEEE DOI Video of talk:
WWW Link. 1006
BibRef

Margolin, R.[Ran], Zelnik-Manor, L.[Lihi], Tal, A.[Ayellet],
Saliency for image manipulation,
VC(29), No. 5, May 2013, pp. 381-392.
WWW Link. 1305
BibRef

Margolin, R.[Ran], Zelnik-Manor, L.[Lihi], Tal, A.[Ayellet],
How to Evaluate Foreground Maps,
CVPR14(248-255)
IEEE DOI 1409
evaluation, foreground extraction, meta-measure, saliency BibRef

Duncan, K.[Kester], Sarkar, S.[Sudeep],
Saliency in images and video: a brief survey,
IET-CV(6), No. 6, 2012, pp. 514-523.
DOI Link 1301
BibRef

Duncan, K.[Kester], Sarkar, S.[Sudeep],
Relational entropy-based saliency detection in images and videos,
ICIP12(1093-1096).
IEEE DOI 1302
BibRef
Earlier:
REM: Relational Entropy-Based Measure of Saliency,
ICCVGIP10(40-47).
DOI Link 1111
BibRef

Xie, Y.L.[Yu-Lin], Lu, H.C.[Hu-Chuan], Yang, M.H.[Ming-Hsuan],
Bayesian Saliency via Low and Mid Level Cues,
IP(22), No. 5, May 2013, pp. 1689-1698.
IEEE DOI 1303
BibRef

Xu, L.F.[Lin-Feng], Li, H.L.[Hong-Liang], Zeng, L.Y.[Liao-Yuan], Ngan, K.N.[King Ngi],
Saliency detection using joint spatial-color constraint and multi-scale segmentation,
JVCIR(24), No. 4, May 2013, pp. 465-476.
Elsevier DOI 1305
Visual attention, Saliency model, Region detection, Human fixation prediction, Spatial constraint, Color double-opponent, Similarity distribution, Multi-scale technique, Segmentation-based method BibRef

Tang, L., Li, H., Wu, Q., Ngan, K.N.[King Ngi],
Boundary-Guided Optimization Framework for Saliency Refinement,
SPLetters(25), No. 4, April 2018, pp. 491-495.
IEEE DOI 1804
convex programming, convolution, feedforward neural nets, image segmentation, object detection, smoothing methods, saliency detection BibRef

Duits, R., Boscain, U., Rossi, F., Sachkov, Y.,
Association Fields via Cuspless Sub-Riemannian Geodesics in SE(2),
JMIV(49), No. 2, June 2014, pp. 384-417.
Springer DOI 1405
Gestalt perception fields. BibRef

Li, J.[Jia], Gao, W.[Wen],
Visual Saliency Computation,


WWW Link. Springer2014. BibRef 1400 LNCS8408. ISBN: 978-3-319-05641-8 1405
BibRef

Zhou, J.B.[Jing-Bo], Gao, S.B.[Shang-Bing], Yan, Y.Y.[Yun-Yang], Jin, Z.[Zhong],
Saliency detection framework via linear neighbourhood propagation,
IET-IPR(8), No. 12, 2014, pp. 804-814.
DOI Link 1412
graph theory BibRef

Chen, C.Z.[Chengli-Zhao], Li, S.[Shuai], Qin, H.[Hong], Hao, A.[Aimin],
Structure-Sensitive Saliency Detection via Multilevel Rank Analysis in Intrinsic Feature Space,
IP(24), No. 8, August 2015, pp. 2303-2316.
IEEE DOI 1505
Correlation BibRef

Chen, C.Z.[Chengli-Zhao], Li, S.[Shuai], Qin, H.[Hong], Hao, A.[Aimin],
Real-Time and Robust Object Tracking in Video via Low-Rank Coherency Analysis in Feature Space,
PR(48), No. 9, 2015, pp. 2885-2905.
Elsevier DOI 1506
Localized compressive sensing representation BibRef

Liang, M., Hu, X.,
Feature Selection in Supervised Saliency Prediction,
Cyber(45), No. 5, May 2015, pp. 900-912.
IEEE DOI 1505
Accuracy BibRef

Ma, X.L.[Xiao-Long], Xie, X.D.[Xu-Dong], Lam, K.M.[Kin-Man], Hu, J.M.[Jian-Ming], Zhong, Y.S.[Yi-Sheng],
Saliency detection based on singular value decomposition,
JVCIR(32), No. 1, 2015, pp. 95-106.
Elsevier DOI 1511
Gaussian filter BibRef

Li, J.[Jia], Fang, S.[Shu], Tian, Y.H.[Yong-Hong], Huang, T.J.[Tie-Jun], Chen, X.W.[Xiao-Wu],
Image saliency estimation via random walk guided by informativeness and latent signal correlations,
SP:IC(38), No. 1, 2015, pp. 3-14.
Elsevier DOI 1512
Image saliency BibRef

Koutras, P.[Petros], Maragos, P.[Petros],
A perceptually based spatio-temporal computational framework for visual saliency estimation,
SP:IC(38), No. 1, 2015, pp. 15-31.
Elsevier DOI 1512
Spatio-temporal visual frontend BibRef

Yu, J.G., Xia, G.S., Gao, C., Samal, A.,
A Computational Model for Object-Based Visual Saliency: Spreading Attention Along Gestalt Cues,
MultMed(18), No. 2, February 2016, pp. 273-286.
IEEE DOI 1601
Biological system modeling BibRef

Niu, Y.Z.[Yu-Zhen], Ke, L.L.[Ling-Ling], Guo, W.Z.[Wen-Zhong],
Evaluation of visual saliency analysis algorithms in noisy images,
MVA(27), No. 5, August 2016, pp. 915-927.
WWW Link. 1609
BibRef

Niu, Y.Z.[Yu-Zhen], Lin, W.Q.[Wen-Qi], Ke, X.[Xiao], Ke, L.L.[Ling-Ling],
Fitting-based optimisation for image visual salient object detection,
IET-CV(11), No. 2, March 2017, pp. 161-172.
DOI Link 1703
BibRef

Niu, Y.Z.[Yu-Zhen], Lin, W.Q.[Wen-Qi], Ke, X.[Xiao],
CF-based optimisation for saliency detection,
IET-CV(12), No. 4, June 2018, pp. 365-376.
DOI Link 1805
BibRef

Chen, Z.H.[Zhi-Hui], Wang, H.Z.[Han-Zi], Zhang, L.M.[Li-Ming], Yan, Y.[Yan], Liao, H.Y.M.[Hong-Yuan Mark],
Visual saliency detection based on homology similarity and an experimental evaluation,
JVCIR(40, Part A), No. 1, 2016, pp. 251-264.
Elsevier DOI 1609
Homology similarity BibRef

Chen, S.H.[Shu-Han], Zheng, L.[Ling], Hu, X.L.[Xue-Long], Zhou, P.[Ping],
Discriminative saliency propagation with sink points,
PR(60), No. 1, 2016, pp. 2-12.
Elsevier DOI 1609
Saliency Detection BibRef

Xu, Y.Y.[Ying-Yue], Hong, X.P.[Xiao-Peng], Liu, X.[Xin], Zhao, G.Y.[Guo-Ying],
Saliency detection via bi-directional propagation,
JVCIR(53), 2018, pp. 113-121.
Elsevier DOI 1805
Saliency detection, Bi-directional, Propagation BibRef

Ling, J.[Jing], Zhang, K.[Kao], Zhang, Y.X.[Ying-Xue], Yang, D.Q.[Dai-Qin], Chen, Z.Z.[Zhen-Zhong],
A saliency prediction model on 360 degree images using color dictionary based sparse representation,
SP:IC(69), 2018, pp. 60-68.
Elsevier DOI 1811
360° images, Saliency prediction, Color dictionary, Sparse representation, Latitude-bias BibRef

Fang, Y.M.[Yu-Ming], Zhang, X.Q.[Xiao-Qiang], Imamoglu, N.[Nevrez],
A novel superpixel-based saliency detection model for 360-degree images,
SP:IC(69), 2018, pp. 1-7.
Elsevier DOI 1811
Visual attention, 360-degree image, Saliency detection, Figure-ground law, Boundary connectivity, Gestalt theory BibRef

Startsev, M.[Mikhail], Dorr, M.[Michael],
360-aware saliency estimation with conventional image saliency predictors,
SP:IC(69), 2018, pp. 43-52.
Elsevier DOI 1811
Saliency prediction, Equirectangular projection, Panoramic images BibRef

Battisti, F.[Federica], Baldoni, S.[Sara], Brizzi, M.[Michele], Carli, M.[Marco],
A feature-based approach for saliency estimation of omni-directional images,
SP:IC(69), 2018, pp. 53-59.
Elsevier DOI 1811
Saliency estimation, Human fixation, Omni-directional images, 360° images BibRef

Jia, N.[Ning], Zhao, W.D.[Wei-Dong], Liu, X.H.[Xian-Hui],
Dempster-Shafer theory-based hierarchical saliency detection,
IJIST(29), No. 3, September 2019, pp. 329-338.
DOI Link 1908
BibRef

Cong, R., Lei, J., Fu, H., Cheng, M., Lin, W., Huang, Q.,
Review of Visual Saliency Detection With Comprehensive Information,
CirSysVideo(29), No. 10, October 2019, pp. 2941-2959.
IEEE DOI 1910
feature extraction, image colour analysis, image motion analysis, image sequences, object detection, spatiotemporal constraint BibRef

Zhang, H.M.[Heng-Min], Qian, J.J.[Jian-Jun], Zhang, B.[Bob], Yang, J.[Jian], Gong, C.[Chen], Wei, Y.[Yang],
Low-Rank Matrix Recovery via Modified Schatten-p Norm Minimization With Convergence Guarantees,
IP(29), 2020, pp. 3132-3142.
IEEE DOI 2002
Low-rank matrix recovery, modified Schatten-p norm, iterative singular value thresholding algorithm, convergence guarantees BibRef

Li, J.X.[Jun-Xia], Ding, J.[Jundi], Yang, J.[Jian],
Visual Salience Learning via Low Rank Matrix Recovery,
ACCV14(III: 112-127).
Springer DOI 1504
BibRef

Yu, H., Zheng, K., Fang, J., Guo, H., Wang, S.,
A New Method and Benchmark for Detecting Co-Saliency Within a Single Image,
MultMed(22), No. 12, December 2020, pp. 3051-3063.
IEEE DOI 2011
Saliency detection, Proposals, Benchmark testing, Object recognition, Streaming media, Image segmentation, low-rank based fusion BibRef

Yamanaka, T.[Takao], Suzuki, T.[Tatsuya], Nobutsune, T.[Taiki], Wu, C.[Chenjunlin],
Multi-Scale Estimation for Omni-Directional Saliency Maps Using Learnable Equator Bias,
IEICE(E106-D), No. 10, October 2023, pp. 1723-1731.
WWW Link. 2310
BibRef

Yang, F.Z.[Fa-Zhan], Guo, X.G.[Xing-Ge], Liang, S.[Song], Zhao, P.P.[Pei-Pei], Li, S.H.[Shan-Hua],
Siamese Transformer for Saliency Prediction Based on Multi-Prior Enhancement and Cross-Modal Attention Collaboration,
IEICE(E106-D), No. 9, September 2023, pp. 1572-1583.
WWW Link. 2310
BibRef


Zhang, J.[Jing], Xie, J.W.[Jian-Wen], Barnes, N.M.[Nick M.], Li, P.[Ping],
An Energy-Based Prior for Generative Saliency,
PAMI(45), No. 11, November 2023, pp. 13100-13116.
IEEE DOI 2310
BibRef

Malladi, S.P.K.[Sai Phani Kumar], Mukherjee, J.[Jayanta], Larabi, M.C.[Mohamed-Chaker], Chaudhury, S.[Santanu],
Towards explainable deep visual saliency models,
CVIU(235), 2023, pp. 103782.
Elsevier DOI 2310
Explainable saliency, Human perception, Log-Gabor filters, Color perception BibRef

Chen, S.[Shi], Valliappan, N.[Nachiappan], Shen, S.L.[Shao-Lei], Ye, X.Y.[Xin-Yu], Kohlhoff, K.[Kai], He, J.F.[Jun-Feng],
Learning from Unique Perspectives: User-aware Saliency Modeling,
CVPR23(2701-2710)
IEEE DOI 2309
BibRef

Zhu, D.D.[Dan-Dan], Chen, Y.Q.[Yong-Qing], Min, X.K.[Xiong-Kuo], Zhao, D.F.[De-Fang], Zhu, Y.C.[Yu-Cheng], Zhou, Q.Q.[Qiang-Qiang], Yang, X.K.[Xiao-Kang], Han, T.[Tian],
Saliency Prediction on Omnidirectional Images with Brain-Like Shallow Neural Network,
ICPR21(1665-1671)
IEEE DOI 2105
Training, Visualization, Biological system modeling, Random access memory, Predictive models, Visual systems, Brain modeling BibRef

Thakur, S.[Shailja], Fischmeister, S.[Sebastian],
A generalizable saliency map-based interpretation of model outcome,
ICPR21(4099-4106)
IEEE DOI 2105
Sensitivity, Neural networks, Machine learning, Data models BibRef

Zhang, Z.[Zhao], Jin, W.[Wenda], Xu, J.[Jun], Cheng, M.M.[Ming-Ming],
Gradient-Induced Co-Saliency Detection,
ECCV20(XII: 455-472).
Springer DOI 2010
BibRef

Fosco, C.[Camilo], Newman, A.[Anelise], Sukhum, P.[Pat], Zhang, Y.B.[Yun Bin], Zhao, N.X.[Nan-Xuan], Oliva, A.[Aude], Bylinskii, Z.[Zoya],
How Much Time Do You Have? Modeling Multi-Duration Saliency,
CVPR20(4472-4481)
IEEE DOI 2008
What you see quickly. Face, Predictive models, Data models, Computational modeling, Visualization, Task analysis BibRef

Tsiami, A., Koutras, P., Maragos, P.,
STAViS: Spatio-Temporal AudioVisual Saliency Network,
CVPR20(4765-4775)
IEEE DOI 2008
Visualization, Videos, Estimation, Fuses, Databases, Task analysis BibRef

Rebuffi, S., Fong, R., Ji, X., Vedaldi, A.,
There and Back Again: Revisiting Backpropagation Saliency Methods,
CVPR20(8836-8845)
IEEE DOI 2008
Computational modeling, Predictive models, Sensitivity, Feature extraction, Backpropagation BibRef

Siris, A., Jiao, J., Tam, G.K.L., Xie, X., Lau, R.W.H.,
Inferring Attention Shift Ranks of Objects for Image Saliency,
CVPR20(12130-12140)
IEEE DOI 2008
Visualization, Observers, Psychology, Object detection, Proposals, Predictive models, Task analysis BibRef

Kim, B., Seo, J., Jeon, S., Koo, J., Choe, J., Jeon, T.,
Why are Saliency Maps Noisy? Cause of and Solution to Noisy Saliency Maps,
VXAI19(4149-4157)
IEEE DOI 2004
backpropagation, feature extraction, image denoising, image segmentation, neural nets, noisy saliency maps, Attribution-Map BibRef

Buzzelli, M.[Marco], Bianco, S.[Simone], Ciocca, G.[Gianluigi],
Combining Saliency Estimation Methods,
CIAP19(II:326-336).
Springer DOI 1909
BibRef

Boccignone, G.[Giuseppe], Cuculo, V.[Vittorio], d'Amelio, A.[Alessandro],
Problems with Saliency Maps,
CIAP19(II:35-46).
Springer DOI 1909
BibRef

Cornia, M., Baraldi, L., Serra, G., Cucchiara, R.,
SAM: Pushing the Limits of Saliency Prediction Models,
WiCV18(1971-19712)
IEEE DOI 1812
Predictive models, Visualization, Measurement, Feature extraction, Computational modeling BibRef

Yohanandan, S.[Shivanthan], Song, A.[Andy], Dyer, A.G.[Adrian G.], Tao, D.C.[Da-Cheng],
Saliency Preservation in Low-Resolution Grayscale Images,
ECCV18(VI: 237-254).
Springer DOI 1810
BibRef

Kümmerer, M.[Matthias], Wallis, T.S.A.[Thomas S. A.], Bethge, M.[Matthias],
Saliency Benchmarking Made Easy: Separating Models, Maps and Metrics,
ECCV18(XVI: 798-814).
Springer DOI 1810
BibRef

Yan, G.[Geng], Wang, Y.[Yang], Liao, Z.C.[Zi-Cheng],
LSTM for Image Annotation with Relative Visual Importance,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Hwang, I., Park, J.S., Park, G.Y., Cho, N.I.,
Image co-saliency detection based on clustering and diffusion process,
VCIP16(1-4)
IEEE DOI 1701
Clustering algorithms BibRef

Wloka, C.[Calden], Tstotsos, J.K.[John K.],
Spatially Binned ROC: A Comprehensive Saliency Metric,
CVPR16(525-534)
IEEE DOI 1612
BibRef

Wang, Q.S.[Qiao-Song], Zheng, W.[Wen], Piramuthu, R.[Robinson],
GraB: Visual Saliency via Novel Graph Model and Background Priors,
CVPR16(535-543)
IEEE DOI 1612
BibRef

Bruce, N.D.B.[Neil D. B.], Catton, C.[Christopher], Janjic, S.[Sasa],
A Deeper Look at Saliency: Feature Contrast, Semantics, and Beyond,
CVPR16(516-524)
IEEE DOI 1612
BibRef

Jiang, M.[Ming], Boix, X.[Xavier], Xu, J.[Juan], Roig, G.[Gemma], Van Gool, L.J.[Luc J.], Zhao, Q.[Qi],
Saliency Prediction with Active Semantic Segmentation,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Sharma, P.[Puneet], Eiksund, O.[Oddmar],
Group Based Asymmetry: A Fast Saliency Algorithm,
ISVC15(I: 901-910).
Springer DOI 1601
BibRef

Kapoor, A.[Aditi], Biswas, K.K., Hanmandlu, M.,
Effective Information and Contrast Based Saliency Detection,
ISVC15(II: 195-204).
Springer DOI 1601
BibRef

Ardizzone, E.[Edoardo], Bruno, A.[Alessandro], Greco, L.[Luca], La Cascia, M.[Marco],
Why You Trust in Visual Saliency,
QoEM15(589-596).
Springer DOI 1511
BibRef

Khatoonabadi, S.H.[Sayed Hossein], Vasconcelos, N.M.[Nuno M.], Bajic, I.V.[Ivan V.], Shan, Y.[Yufeng],
How many bits does it take for a stimulus to be salient?,
CVPR15(5501-5510)
IEEE DOI 1510
BibRef

Mauthner, T.[Thomas], Possegger, H.[Horst], Waltner, G.[Georg], Bischof, H.[Horst],
Encoding based saliency detection for videos and images,
CVPR15(2494-2502)
IEEE DOI 1510
BibRef

Gong, C.[Chen], Tao, D.C.[Da-Cheng], Liu, W.[Wei], Maybank, S.J., Fang, M.[Meng], Fu, K.[Keren], Yang, J.[Jie],
Saliency propagation from simple to difficult,
CVPR15(2531-2539)
IEEE DOI 1510
BibRef

Jiang, M.[Ming], Huang, S.S.[Sheng-Sheng], Duan, J.Y.[Juan-Yong], Zhao, Q.[Qi],
SALICON: Saliency in Context,
CVPR15(1072-1080)
IEEE DOI 1510
BibRef

Frintrop, S.[Simone], Werner, T.[Thomas], Garcia, G.M.[German M.],
Traditional saliency reloaded: A good old model in new shape,
CVPR15(82-90)
IEEE DOI 1510
BibRef

Sun, X.S.[Xiao-Shuai], Yao, H.X.[Hong-Xun],
Exploring covert attention for generic boosting of saliency models,
ICIP14(1179-1183)
IEEE DOI 1502
Boosting BibRef

Blusseau, S., Carboni, A., Maiche, A., Morel, J.M., von Gioi, R.G.[R. Grompone],
A psychophysical evaluation of the a contrario detection theory,
ICIP14(1091-1095)
IEEE DOI 1502
Brain models. observed configuration is relevant only when it is unlikely to appear just by chance. BibRef

Rahman, S.[Shafin], Rochan, M.[Mrigank], Wang, Y.[Yang], Bruce, N.D.B.[Neil D.B.],
Examining visual saliency prediction in naturalistic scenes,
ICIP14(4082-4086)
IEEE DOI 1502
Benchmark testing BibRef

Guo, D.Y.[Dong-Yan], Zhang, J.[Jian], Xu, M.[Min], He, X.J.[Xiang-Jian], Li, M.X.[Min-Xian], Zhao, C.X.[Chun-Xia],
A Multiple Features Distance Preserving (MFDP) Model for Saliency Detection,
DICTA14(1-7)
IEEE DOI 1502
feature extraction BibRef

Heo, B.[Byeongho], Jeong, H.[Hawook], Kim, J.[Jiyun], Choi, S.I.[Sang-Il], Choi, J.Y.[Jin Young],
Weighted Pooling Based on Visual Saliency for Image Classification,
ISVC14(I: 647-657).
Springer DOI 1501
BibRef

Nakajima, J.[Jiro], Sugimoto, A.[Akihiro], Kawamoto, K.[Kazuhiko],
Incorporating Audio Signals into Constructing a Visual Saliency Map,
PSIVT13(468-480).
Springer DOI 1402
BibRef

Alsam, A.[Ali], Sharma, P.[Puneet],
Validating the Visual Saliency Model,
SCIA13(153-161).
Springer DOI 1311
BibRef

Alsam, A.[Ali], Sharma, P.[Puneet], Wrålsen, A.[Anette],
Asymmetry as a Measure of Visual Saliency,
SCIA13(591-600).
Springer DOI 1311
BibRef

Varadarajan, K.M.[Karthik Mahesh], Vincze, M.[Markus],
Semantic saliency using k-TR theory of visual perception,
ICPR12(3676-3679).
WWW Link. 1302
BibRef

Zhu, H.[Hao], Han, B.[Biao], Ruan, X.[Xiang],
Visual saliency: A manifold way of perception,
ICPR12(2606-2609).
WWW Link. 1302
BibRef

Huang, J.B.[Jia-Bin], Ahuja, N.[Narendra],
Saliency detection via divergence analysis: A unified perspective,
ICPR12(2748-2751).
WWW Link. 1302
BibRef

Laine-Hernandez, M., Kinnunen, T., Kamarainen, J.K., Lensu, L., Kalviainen, H., Oittinen, P.,
Visual saliency and categorisation of abstract images,
ICPR12(2752-2755).
WWW Link. 1302
BibRef

Grant, S.[Shane], Itti, L.[Laurent],
Saliency mapping enhanced by symmetry from local phase,
ICIP12(653-656).
IEEE DOI 1302
BibRef

Lang, C.Y.[Cong-Yan], Nguyen, T.V.[Tam V.], Katti, H.[Harish], Yadati, K.[Karthik], Kankanhalli, M.[Mohan], Yan, S.C.[Shui-Cheng],
Depth Matters: Influence of Depth Cues on Visual Saliency,
ECCV12(II: 101-115).
Springer DOI 1210
BibRef

Niu, Y.Z.[Yu-Zhen], Geng, Y.J.[Yu-Jie], Li, X.Q.[Xue-Qing], Liu, F.[Feng],
Leveraging stereopsis for saliency analysis,
CVPR12(454-461).
IEEE DOI 1208
BibRef

Schauerte, B.[Boris], Stiefelhagen, R.[Rainer],
Predicting human gaze using quaternion DCT image signature saliency and face detection,
WACV12(137-144).
IEEE DOI 1203
Award, WACV, Student. Visual saliency, where people will look at an image. DCT Saliency Map. Bruce-Tsotsos. BibRef

Loog, M.[Marco],
Information theoretic preattentive saliency: A closed-form solution,
ITCVPR11(1418-1424).
IEEE DOI 1201
BibRef

Ardizzone, E.[Edoardo], Bruno, A.[Alessandro], Gugliuzza, F.[Francesco],
Exploiting Visual Saliency Algorithms for Object-Based Attention: A New Color and Scale-Based Approach,
CIAP17(II:191-201).
Springer DOI 1711
BibRef

Ardizzone, E.[Edoardo], Bruno, A.[Alessandro],
Saliency Based Image Cropping,
CIAP13(I:773-782).
Springer DOI 1311
BibRef

Ardizzone, E.[Edoardo], Bruno, A.[Alessandro], Mazzola, G.[Giuseppe],
Visual Saliency by Keypoints Distribution Analysis,
CIAP11(I: 691-699).
Springer DOI 1109

See also Detecting multiple copies in tampered images. BibRef

Rezazadegan Tavakoli, H.[Hamed], Rahtu, E.[Esa], Heikkilä, J.[Janne],
Analysis of Sampling Techniques for Learning Binarized Statistical Image Features Using Fixations and Salience,
CVLBP14(124-134).
Springer DOI 1504
BibRef
And:
Saliency Detection Using Joint Temporal and Spatial Decorrelation,
SCIA13(707-717).
Springer DOI 1311
BibRef
And:
Spherical Center-Surround for Video Saliency Detection Using Sparse Sampling,
ACIVS13(695-704).
Springer DOI 1311
BibRef
Earlier:
Temporal Saliency for Fast Motion Detection,
BMC12(I:321-326).
Springer DOI 1304
BibRef
Earlier:
Fast and Efficient Saliency Detection Using Sparse Sampling and Kernel Density Estimation,
SCIA11(666-675).
Springer DOI 1105
BibRef

Wang, W.[Wei], Wang, Y.Z.[Yi-Zhou], Huang, Q.M.[Qing-Ming], Gao, W.[Wen],
Measuring visual saliency by Site Entropy Rate,
CVPR10(2368-2375).
IEEE DOI Video of talk:
WWW Link. 1006
BibRef

Shi, X.[Xun], Wang, B.[Bo], Tsotsos, J.K.[John K.],
Early Recurrence Improves Edge Detection,
BMVC13(xx-yy).
DOI Link 1402
BibRef
Earlier: A1, A3, Only:
Improved Edge Representation via Early Recurrent Inhibition,
CRV12(40-47).
IEEE DOI 1207
BibRef

Bruce, N.D.B.[Neil D.B.], Shi, X.[Xun], Tsotsos, J.K.[John K.],
Recurrent Refinement for Visual Saliency Estimation in Surveillance Scenarios,
CRV12(117-124).
IEEE DOI 1207
BibRef

Bruce, N.D.B.[Neil D.B.], Shi, X.[Xun], Simine, E.[Evgueni], Tsotsos, J.K.[John K.],
Visual Representation in the Determination of Saliency,
CRV11(242-249).
IEEE DOI 1105
BibRef

Shi, X.[Xun], Bruce, N.D.B.[Neil D.B.], Tsotsos, J.K.[John K.],
Biologically Motivated Local Contextual Modulation Improves Low-Level Visual Feature Representations,
ICIAR12(I: 79-88).
Springer DOI 1206
BibRef
Earlier:
Fast, recurrent, attentional modulation improves saliency representation and scene recognition,
WBCV11(1-8).
IEEE DOI 1106
BibRef

Bruce, N.D.B.[Neil D.B.], Kornprobst, P.[Pierre],
On the role of context in probabilistic models of visual saliency,
ICIP09(3089-3092).
IEEE DOI 0911
BibRef

Li, Y.[Yin], Zhou, Y.[Yue], Xu, L.[Lei], Yang, X.C.[Xiao-Chao], Yang, J.[Jie],
Incremental sparse saliency detection,
ICIP09(3093-3096).
IEEE DOI 0911
BibRef

Wong, L.K.[Lai-Kuan], Low, K.L.[Kok-Lim],
Saliency retargeting: An approach to enhance image aesthetics,
WACV11(73-80).
IEEE DOI 1101
BibRef
Earlier:
Saliency-enhanced image aesthetics class prediction,
ICIP09(997-1000).
IEEE DOI 0911
BibRef

Bileschi, S.M.[Stanley M.], Wolf, L.B.[Lior B.],
Image representations beyond histograms of gradients: The role of Gestalt descriptors,
CVPR07(1-8).
IEEE DOI 0706
BibRef

Pila, M.,
The saliency grouping field,
ICIP04(IV: 2307-2310).
IEEE DOI 0505
BibRef

Kadir, T.[Timor], Zisserman, A.[Andrew], Brady, M.[Michael],
An Affine Invariant Salient Region Detector,
ECCV04(Vol I: 228-241).
Springer DOI 0405
BibRef

Perona, P.[Pietro],
The Emergence of Visual Categories: A Computational Perspective,
SCIA03(1-3).
Springer DOI 0310
BibRef

Yu, S.,
Computational Models of Perceptual Organization,
CMU-RI-TR-03-14, May, 2003. BibRef 0305 Ph.D.Thesis
HTML Version. 0306
BibRef

Yu, S.X.[Stella X.], Lisin, D.A.[Dimitri A.],
Image Compression Based on Visual Saliency at Individual Scales,
ISVC09(I: 157-166).
Springer DOI 0911
BibRef

Lisin, D.A.[Dimitri A.], Riseman, E.M.[Edward M.], Hanson, A.R.[Allen R.],
Extracting Salient Image Features for Reliable Matching Using Outlier Detection Techniques,
CVS03(481 ff).
Springer DOI 0306
BibRef

Lisin, D.A., Riseman, E.M., Hanson, A.R.,
Extracting Salient Image Features Using Outlier Detection Techniques,
UMassCS TR 2003-2, January, 2003.
PS File. BibRef 0301

Johannes, M.[Marc], Sebastian, T.B.[Thomas B.], Tek, H.[Huseyin], Kimia, B.B.[Benjamin B.],
Perceptual Organization as Object Recognition Divided by Two,
PercOrg01(xx-yy). 0106
BibRef

Sakai, K.[Ko],
A Network Mechanism for the Determination of Apparent Orientation,
ICPR00(Vol III: 955-958).
IEEE DOI 0009
BibRef

Wong, H., Guan, L.,
Characterization of Perceptual Importance for Object-based Image Segmentation,
ICIP00(Vol III: 54-57).
IEEE DOI 0008
BibRef

Younes, L.,
Calibrating Parameters of Cost Functionals,
ECCV00(II: 212-223).
Springer DOI 0003
BibRef

Milanese, R.,
Detecting Salient Regions in an Image: From Biology to Implementation,
Ph.D.Thesis, Univ. of Geneva, Switzerland, 1993. BibRef 9300

Pardo, X.M.[Xosé M.], Fdez-Vidal, X.R.[Xosé R.],
What Do Datasets Say About Saliency Models?,
IbPRIA17(104-113).
Springer DOI 1706
BibRef

Rodriguez Sánchez, R., García, J.A., Fdez Valdivia, J., Fdez Vidal, X.R.,
Illusory Percepts Through a Constraint of Invariance in Integral Features Across Frequency Bands,
SCIA99(Pattern Recognition II). BibRef 9900

Walker, K.N., Cootes, T.F., Taylor, C.J.,
Locating Salient Facial Features Using Image Invariants,
AFGR98(242-247).
IEEE DOI BibRef 9800

Walker, K.N., Cootes, T.F., Taylor, C.J.,
Locating Salient Object Features,
BMVC98(xx-yy). BibRef 9800

Perona, P., Freeman, W.,
A Factorization Approach to Grouping,
ECCV98(I: 655-670).
Springer DOI BibRef 9800

Geiger, D.[Davi], Pao, H.K.[Hsing-Kuo], Rubin, N.,
Salient and Multiple Illusory Surfaces,
CVPR98(118-124).
IEEE DOI BibRef 9800

Alquier, L.[Laurent], and Montesinos, P.[Philippe],
Recursive Perceptual Grouping for 3D Object Reconstruction from 2D Scenes,
SCIA97(xx-yy)
HTML Version. 9705
BibRef

Schack, B., Krause, W.,
Instantaneous Coherence as a Sensible Parameter for Considering Human Information Processing,
ICPR96(II: 45-49).
IEEE DOI 9608
(Klinikum der Friedrich Schiller-Univ, D) BibRef

Chapter on Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar continues in
Perceptual Grouping, Saliency, General Systems .


Last update:Mar 16, 2024 at 20:36:19