End-to-end saliency mapping via probability distribution prediction

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 2016-December, Issue , 2016, Pages 5753-5761

  Jetley, Saumya ^a   Murray, Naila ^b   Vig, Eleonora ^b  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b XEROX RESEARCH CENTRE EUROPE   (France)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; COMPUTER VISION; PATTERN RECOGNITION;

ACTIVATION FUNCTIONS; BENCHMARK DATASETS; BERNOULLI DISTRIBUTIONS; DATA-DRIVEN METHODS; DEEP ARCHITECTURES; ESTIMATION METHODS; LARGE-SCALE DATASETS; TOPOGRAPHICAL MAPS;

PROBABILITY DISTRIBUTIONS;

EID: 84986320384     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2016.620     Document Type: Conference Paper

References (48)

1. R. Achanta and S. Süsstrunk. Saliency detection for contentaware image resizing. In ICIP, 2009.
2. A. Borji and L. Itti. State-of-the-art in visual attention modeling. TPAMI, 2013.
3. A. Borji and J. Tanner. Reconciling saliency and object center-bias hypotheses in explaining free-viewing fixations. IEEE TNNLS, 2015.
4. N. Bruce and J. Tsotsos. Saliency based on information maximization. In NIPS, 2006.
5. Z. Bylinskii, T. Judd, A. Borji, L. Itti, F. Durand, A. Oliva, and A. Torralba. Mit saliency benchmark. http://saliency. mit.edu/.
6. M. Cerf, J. Harel, W. Einhäuser, and C. Koch. Predicting human gaze using low-level saliency combined with face detection. In NIPS, 2008.
7. S. O. Gilani, R. Subramanian, Y. Yan, D. Melcher, N. Sebe, and S. Winkler. PET: An eye-tracking dataset for animalcentric pascal object classes. In ICME, 2015.
8. J. Harel, C. Koch, and P. Perona. Graph-based visual saliency. In NIPS, 2006. 2, 6, 7
9. X. Hou, J. Harel, and C. Koch. Image signature: Highlighting sparse salient regions. TPAMI, 2012.
10. X. Hou and L. Zhang. Saliency detection: A spectral residual approach. In CVPR, 2007.
11. X. Huang, C. Shen, X. Boix, and Q. Zhao. Salicon: Reducing the semantic gap in saliency prediction by adapting deep neural networks. In Proceedings of the IEEE International Conference on Computer Vision, pages 262-270, 2015.
12. L. Itti and P. F. Baldi. Bayesian surprise attracts human attention. In NIPS, 2006.
13. L. Itti and C. Koch. A saliency-based search mechanism for overt and covert shifts of visual attention. Vision research, 40 (10): 1489-1506, 2000.
14. L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. TPAMI, (11): 1254-1259, 1998.
15. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutional architecture for fast feature embedding. In ACM MM, 2014.
16. M. Jiang, S. Huang, J. Duan, and Q. Zhao. SALICON: Saliency in Context. In CVPR, 2015.
17. T. Judd, F. Durand, and A. Torralba. A benchmark of computational models of saliency to predict human fixations. In MIT Technical Report, 2012.
18. T. Judd, K. Ehinger, F. Durand, and A. Torralba. Learning to predict where humans look. In CVPR, 2009.
19. W. Kienzle, F. A. Wichmann, B. Schölkopf, and M. O. Franz. A Nonparametric Approach to Bottom-Up Visual Saliency. In NIPS, 2007.
20. C. Koch and S. Ullman. Shifts in selective visual attention: Towards the underlying neural circuitry. In Matters of intelligence, pages 115-141. 1987.
21. A. Krizhevsky, I. Sutskever, and G. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012.
22. S. S. S. Kruthiventi, K. Ayush, and R. V. Babu. Deepfix: A fully convolutional neural network for predicting human eye fixations. Technical report, 2015. ArXiv: 1510. 02927.
23. M. Kümmerer, L. Theis, and M. Bethge. Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet. In ICLR Workshop, 2015.
24. C. Lang, T. Nguyen, H. Katti, K. Yadati, M. Kankanhalli, and S. Yan. Depth matters: Influence of depth cues on visual saliency. In ECCV, 2012.
25. T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick. Microsoft COCO: Common objects in context. In ECCV, 2014.
26. Z. Liu, O. Le Meur, S. Luo, and L. Shen. Saliency detection using regional histograms. Optics letters, 38 (5): 700-702, 2013.
27. J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In CVPR, June 2015.
28. Y. Luo, Y. Wong, and Q. Zhao. Label consistent quadratic surrogate model for visual saliency prediction. In CVPR, 2015.
29. L. Marchesotti, C. Cifarelli, and G. Csurka. A framework for visual saliency detection with applications to image thumbnailing. In ICCV, 2009.
30. S. Mathe and C. Sminchisescu. Action from still image dataset and inverse optimal control to learn task specific visual scanpaths. In NIPS, 2013.
31. N. Murray, M. Vanrell, X. Otazu, and C. A. Parraga. Saliency estimation using a non-parametric low-level vision model. In CVPR, 2011.
32. N. Murray, M. Vanrell, X. Otazu, and C. A. Parraga. Low-level spatiochromatic grouping for saliency estimation. TPAMI, 2013.
33. J. Pan and X. G. i Nieto. End-to-end convolutional network for saliency prediction. Technical report, 2015. ArXiv: 1507. 01422.
34. S. Ren, K. He, R. B. Girshick, and J. Sun. Faster R-CNN: Towards real-time object detection with region proposal networks. CoRR, abs/1506. 01497, 2015.
35. B. Schauerte and R. Stiefelhagen. Quaternion-based spectral saliency detection for eye fixation prediction. In ECCV, pages 116-129, 2012.
36. G. Sharma, F. Jurie, and C. Schmid. Discriminative spatial saliency for image classification. In CVPR, 2012.
37. C. Shen, M. Song, and Q. Zhao. Learning high-level concepts by training a deep network on eye fixations. In NIPS Deep Learning and Unsupervised Feature Learning Workshop, 2012.
38. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In ICLR, 2015.
39. F. Stentiford. Attention based auto image cropping. In The 5th International Conference on Computer Vision Systems, Bielefeld, 2007.
40. A. M. Treisman and G. Gelade. A feature-integration theory of attention. Cognitive psychology, 12 (1): 97-136, 1980.
41. R. Valenti, N. Sebe, and T. Gevers. Image saliency by isocentric curvedness and color. In ICCV, 2009.
42. E. Vig, M. Dorr, and D. Cox. Large-scale optimization of hierarchical features for saliency prediction in natural images. In CVPR, 2014.
43. P. Xu, K. A. Ehinger, Y. Zhang, A. Finkelstein, S. R. Kulkarni, and J. Xiao. Turkergaze: Crowdsourcing saliency with webcam based eye tracking. Technical report, 2015. ArXiv: 1504. 06755v1.
44. W. H. Zangemeister, H. Stiehl, and C. Freksa. Visual attention and cognition. Elsevier, 1996.
45. J. Zhang and S. Sclaroff. Saliency detection: A boolean map approach. In ICCV, 2013.
46. L. Zhang, M. H. Tong, T. K. Marks, H. Shan, and G. W. Cottrell. SUN: A Bayesian framework for saliency using natural statistics. JoV, 8 (7): 1-20, 12 2008.
47. Y. Zhang, F. Yu, S. Song, P. Xu, A. Seff, and J. Xiao. Large-scale scene understanding challenge. http://lsun. cs. princeton.edu/leaderboard/#saliencysalicon.
48. Q. Zhao and C. Koch. Learning a saliency map using fixated locations in natural scenes. JoV, 11 (3), 2011.