Saliency detection based on proto-objects and topic model

2011 IEEE Workshop on Applications of Computer Vision, WACV 2011

Volumn , Issue , 2011, Pages 125-131

  Li, Zhidong ^a   Xu, Jie ^a   Wang, Yang ^a   Geers, Glenn ^a   Yang, Jun ^a  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL FRAMEWORK; DATA SETS; LATENT TOPIC MODEL; OBJECTS DETECTION; SALIENCY DETECTION; SALIENCY MAP; STATE-OF-ART METHODS; TOPIC MODEL;

COMPUTER VISION;

COMPUTER APPLICATIONS;

EID: 79952501767     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WACV.2011.5711493     Document Type: Conference Paper

References (25)

1. L. Itti, C. Koch and E. Niebur, A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Analysis and Machine Intelligence, 1998.
2. Z. Li, Y. Wang, J. Chen, J. Xu and J. Larid, Image topic discovery with saliency detection, in British Machine Vision Conference, 2010.
3. C. Siagian and L. Itti, Rapid biologically-inspired scene classification using features shared with visual attention. IEEE Trans. Pattern Analysis and Machine Intelligence, 2007.
4. G. Underwood, T. Foulsham, E.v. Loon, L. Humphreys and J. Bloyce, Eye movements during scene inspection: A test of the saliency map hypothesis. European Journal Of Cognitive Psychology, 2006.
5. D. Walther and C. Kochb, Modeling attention to salient proto-objects. Neural Networks, 2006.
6. T. Liu, J. Sun, N.-N. Zheng, X. Tang and H.-Y. Shum. Learning to detect a salient object. in IEEE Conf. Computer Vision and Pattern Recognition, 2007.
7. Y.F. Ma and H.J. Zhang. Contrast-based image attention analysis by using fuzzy growing. in ACM Multimedia 2003.
8. B. Alexe, T. Deselaers and V. Ferrari, What is an object. in IEEE Conf. Computer Vision and Pattern Recognition, 2010.
9. X. Hou and L. Zhang. Saliency detection: a spectral residual approach. in IEEE Conf. Computer Vision and Pattern Recognition, 2007.
10. A. Oliva, A. Torralba, M. Castelhano and J. Henderson, Top-Down control of visual attention in object detection, in IEEE Conf. International Conference on Image Processing, 2003.
11. V. Navalpakkam and L. Itti, An integrated model of top-down and bottom-up attention for optimal object detection, in IEEE Conf. Computer Vision and Pattern Recognition, 2006.
12. R. Achantay, S. Hemamiz, F. Estraday and S. Süsstrunk, Frequency-tuned salient region detection, in in IEEE Conf. Computer Vision and Pattern Recognition, 2009.
13. B.C. Russell, A.A. Efros, J. Sivic, W.T. Freeman and A. Zisserman. Using multiple segmentations to discover objects and their extent in image collections. in IEEE Conf. Computer Vision and Pattern Recognition, 2006.
14. J. Harel, C. Koch and P. Perona, Graph-Based visual saliency. in Proceedings of Neural Information Processing Systems Conference, 2007.
15. N.D.B. Bruce and J.K. Tsotsos, Saliency based on information maximization. in Proceedings of Neural Information Processing Systems Conference, 2006.
16. W. Wang and Y. Wang, Measuring Visual Saliency by Site Entropy Rate. in IEEE Conf. Computer Vision and Pattern Recognition, 2010.
17. C. Guo, Q. Ma and L. Zhang. Spatio-Temporal saliency detection using phase spectrum of quaternion fourier transform. in IEEE Conf. Computer Vision and Pattern Recognition, 2008.
18. L. Zhang, M.H. Tong, T.K. Marks, H. Shan and G.W. Cottrell, SUN: A bayesian framework for saliency using natural statistics. Journal of Vision, 2008.
19. Y. Sun, R. Fisher, F. Wang and H.M. Gomes, A computer vision model for visual-object-based attention and eye movements. Computer Vision and Image Understanding, 2008.
20. F. Orabona, G. Metta and G. Sandini, A proto-object based visual attention model, in International Workshop on Attention in Cognitive Systems. 2008.
21. Y. Deng and B.S. Manjunath, Unsupervised segmentation of color-texture regions in images and video. IEEE Trans. Pattern Analysis and Machine Intelligence, 2001.
22. T. Hofmann. Probabilistic latent semantic indexing. in ACM Special Interest Group on Information Retrieval. 1999.
23. C. Rother, V. Kolmogorov and A. Blake, Grabcut - interactive foreground extraction using iterated graph cuts, in ACM Siggraph. 2004.
24. J. Winn, A. Criminisi and T. Minka. Object categorization by learned universal visual dictionary. in International Conference on Comput Vision. 2005.
25. A. Treisman and G. Gelade, A feature-integration theory of attention. Cognitive Psychology, 1980.