Salient pattern detection using W 2 on multivariate normal distributions

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7476 LNCS, Issue , 2012, Pages 246-255

  Klein, Dominik Alexander ^a   Frintrop, Simone ^a  

^a UNIVERSITY OF BONN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

A-CENTER; CLOSED FORM SOLUTIONS; CONTINUOUS DISTRIBUTION; EUCLIDEAN NORM; GAUSSIANS; HUMAN PERCEPTION; LOCAL CENTERS; LOCAL FEATURE; MULTIPLE SCALE; MULTIVARIATE NORMAL DISTRIBUTIONS; PATTERN DETECTION; PRECISION AND RECALL; PRIOR KNOWLEDGE; PROBABILITY THEORY; SALIENT OBJECT DETECTION; VISUAL ATTENTION SYSTEMS; WASSERSTEIN METRIC;

NORMAL DISTRIBUTION; OBJECT RECOGNITION;

DETECTORS;

EID: 84865807679     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-32717-9_25     Document Type: Conference Paper

References (23)

1. Achanta, R., Hemami, S., Estrada, F., Süsstrunk, S.: Frequency-tuned salient region detection. In: CVPR (2009)
2. Achanta, R., Süsstrunk, S.: Saliency detection using maximum symmetric surround. In: Proc. of Int'l Conf. on Image Processing, ICIP (2010)
3. Bruce, N., Tsotsos, J.: Saliency, attention, and visual search: An information theoretic approach. Journal of Vision 9(3), 1-24 (2009)
4. Frintrop, S.: VOCUS: A Visual Attention System for Object Detection and Goal-Directed Search. LNCS (LNAI), vol. 3899. Springer, Heidelberg (2006)
5. Frintrop, S., Rome, E., Christensen, H.I.: Computational visual attention systems and their cognitive foundation: A survey. ACM Trans. on Applied Perception 7(1) (2010)
6. Gao, D., Vasconcelos, N.: Bottom-up saliency is a discriminant process. In: Proc. of ICCV (2007)
7. Givens, C.R., Shortt, R.M.: A class of wasserstein metrics for probability distributions. Michigan Math. J. 31(2) (1984)
8. Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: Proc. of CVPR (2007)
9. Hou, X., Zhang, L.: Dynamic visual attention: Searching for coding length increments. In: Advances in Neural Information Processing Systems (2008)
10. Hurvich, L., Jameson, D.: An opponent-process theory of color vision. Psychological Review 64(6) (1957)
11. Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. on PAMI 20(11) (1998)
12. Klein, D.A., Frintrop, S.: Center-surround divergence of feature statistics for salient object detection. In: Proc. of Int'l Conf. on Computer Vision, ICCV (2011)
13. Kovesi, P.: Arbitrary gaussian filtering with 25 additions and 5 multiplications per pixel. Tech. Rep. UWA-CSSE-09-002, School of Computer Science, University of Western Australia (2009)
14. Lin, Y., Fang, B., Tang, Y.: A computational model for saliency maps by using local entropy. In: Proc. of AAAI (2010)
15. Liu, T., Yuan, Z., Sun, J., Wang, J., Zheng, N., Tang, X., Shum, H.Y.: Learning to detect a salient object. IEEE Trans. on PAMI (2009)
16. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int'l J. of Computer Vision (IJCV) 60(2), 91-110 (2004)
17. Ma, Y.F., Zhang, H.J.: Contrast-based image attention analysis by using fuzzy growing. In: ACM Int'l Conf. on Multimedia (2003)
18. Ruesch, J., Lopes, M., Bernardino, A., Hörnstein, J., Santos-Victor, J., Pfeifer, R.: Multimodal saliency-based bottom-up attention: A framework for the humanoid robot icub. In: Proc. of Int'l Conf. on Robotics and Automation, ICRA (2008)
19. Schauerte, B., Kühn, B., Kroschel, K., Stiefelhagen, R.: Multimodal saliency-based attention for object-based scene analysis. In: Proc. of Int'l Conf. on Intelligent Robots and Systems, IROS (2011)
20. Treisman, A.M., Gelade, G.: A feature integration theory of attention. Cognitive Psychology 12, 97-136 (1980)
21. Walther, D., Koch, C.: Modeling attention to salient proto-objects. Neural Networks (2006)
22. Wolfe, J.M.: Guided search 2.0: A revised model of visual search. Psychonomic Bulletin and Review 1(2) (1994)
23. Wolfe, J.M., Horowitz, T.S.: What attributes guide the deployment of visual attention and how do they do it? Nature Reviews Neuroscience 5, 1-7 (2004)