Removing label ambiguity in learning-based visual saliency estimation

IEEE Transactions on Image Processing

Volumn 21, Issue 4, 2012, Pages 1513-1525

  Li, Jia ^a   Xu, Dong ^a   Gao, Wen ^b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b PEKING UNIVERSITY   (China)

Author keywords

Label ambiguity; learning to rank; multi instance learning (MIL); visual saliency

Indexed keywords

EYE TRACKING DEVICES; FEASIBLE SOLUTION; IMAGE PATCHES; LEARNING TO RANK; MAPPING MODEL; MULTI-INSTANCE LEARNING; MULTIMEDIA APPLICATIONS; MUTUAL CORRELATIONS; ORDINAL REGRESSION; PUBLIC IMAGE; RANKING MODEL; RELABELING; STATE-OF-THE-ART METHODS; TRAINING DATA; USER DATA; VISUAL SALIENCY;

ESTIMATION;

VISUALIZATION;

ALGORITHM; ARTICLE; ARTIFACT; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; DOCUMENTATION; HUMAN; IMAGE ENHANCEMENT; INFORMATION RETRIEVAL; METHODOLOGY; PHYSIOLOGY; VISION;

ALGORITHMS; ARTIFACTS; ARTIFICIAL INTELLIGENCE; DOCUMENTATION; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INFORMATION STORAGE AND RETRIEVAL; PATTERN RECOGNITION, AUTOMATED; VISUAL PERCEPTION;

EID: 84859075549     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2011.2179665     Document Type: Article

References (49)

1. F. Liu and M. Gleicher, "Video retargeting: Automating pan and scan," in Proc. ACM Int. Conf. Multimedia, 2006, pp. 241-250. (Pubitemid 47113539)
2. V. Setlur, T. Lechner,M. Nienhaus, and B. Gooch, "Retargeting images and video for preserving information saliency," IEEE Comput. Graph. Appl., vol. 27, no. 5, pp. 80-88, Sep./Oct. 2007. (Pubitemid 47434072)
3. T. Mei, X.-S. Hua, and S. Li, "Contextual in-image advertising," in Proc. ACM Int. Conf. Multimedia, 2008, pp. 439-448.
4. T. Mei, X.-S. Hua, L. Yang, and S. Li, "Videosense-Towards effective online video advertising," in Proc. ACM Int. Conf. Multimedia, 2007, pp. 1075-1084.
5. J. You, G. Liu, L. Sun, and H. Li, "A multiple visual models based perceptive analysis framework for multilevel video summarization," IEEE Trans. Circuits Syst. Video Technol., vol. 17, no. 3, pp. 273-285, Mar. 2007. (Pubitemid 46394438)
6. Y.-F. Ma, X.-S. Hua, L. Lu, and H.-J. Zhang, "A generic framework of user attention model and its application in video summarization," IEEE Trans. Multimedia, vol. 7, no. 5, pp. 907-919, Oct. 2005. (Pubitemid 41452519)
7. L. Elazary and L. Itti, "Interesting objects are visually salient," J. Vis., vol. 8, no. 3, pp. 1-15, 2008.
8. T. Liu, J. Sun, N.-N. Zheng, X. Tang, and H.-Y. Shum, "Learning to detect a salient object," in Proc. IEEE Conf. Comput. Vis. Pattern Recogn., 2007, pp. 1-8.
9. S. Andrews, I. Tsochantaridis, and T. Hofmann, "Support vector machines for multiple-instance learning," in AdVances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2003, pp. 561-568.
10. L. Itti, C.Koch, and E. Niebur, "A model of saliency-based visual attention for rapid scene analysis," IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 11, pp. 1254-1259, Nov. 1998. (Pubitemid 128742001)
11. X. Hou and L. Zhang, "Saliency detection: A spectral residual approach," in Proc. IEEE Conf. Comput. Vis. Pattern Recogn., 2007, pp. 1-8.
12. J. Harel, C. Koch, and P. Perona, "Graph-based visual saliency," in Proc. Adv. Neural Inf. Process. Syst., 2007, pp. 545-552.
13. Wang W., Y.Wang, Q. Huang, and W. Gao, "Measuring visual saliency by site entropy rate," in Proc. IEEE Conf. Comput. Vis. Pattern Recogn., 2010, pp. 2368-2375.
14. S. Goferman, L. Zelnik-Manor, and A. Tal, "Context-aware saliency detection," in Proc. IEEE Conf. Comput. Vis. Pattern Recogn., 2010, pp. 2376-2383.
15. M.-M. Cheng, G.-X. Zhang, N. J. Mitra, X. Huang, and S.-M. Hu, "Global contrast based salient region detection," in Proc. IEEE Conf. Comput. Vis. Pattern Recogn., 2011, pp. 409-416.
16. R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk, "Frequencytuned salient region detection," in Proc. IEEE Conf. Comput. Vis. Pattern Recogn., 2009, pp. 1597-1604.
17. R. J. Peters and L. Itti, "Beyond bottom-up: Incorporating task-dependent influences into a computational model of spatial attention," in Proc. IEEE Conf. Comput. Vis. Pattern Recogn., 2007, pp. 1-8.
18. T. Judd, K. Ehinger, F. Durand, and A. Torralba, "Learning to predict where humans look," in Proc. IEEE Int. Conf. Comput. Vis., 2009, pp. 2106-2113.
19. V. Navalpakkam and L. Itti, "Search goal tunes visual features optimally," Neuron, vol. 53, no. 4, pp. 605-617, Feb. 2007. (Pubitemid 46216750)
20. Y. Freund, R. Iyer, R. E. Schapire, Y. Singer, and G. Dietterich, "An efficient boosting algorithm for combining preferences," J. Mach. Learn. Res., vol. 4, pp. 933-969, 2003.
21. B. W. Tatler, R. J. Baddeley, and I. D. Gilchrist, "Visual correlates of fixation selection: Effects of scale and time," Vis. Res., vol. 45, no. 5, pp. 643-659, Mar. 2005. (Pubitemid 40037974)
22. L. Itti, G. Rees, and J. Tsotsos, Neurobiology of Attention. San Diego, CA: Elsevier, 2005.
23. S. Marat, T. H. Phuoc, L. Granjon, N. Guyader, D. Pellerin, and A. Guerin-Dugue, "Modelling spatio-temporal saliency to predict gaze direction for short videos," Int. J. Comput. Vis., vol. 82, no. 3, pp. 231-243, May 2009.
24. A. M. Treisman and G. Gelade, "A feature-integration theory of attention," Cogn. Psychol., vol. 12, no. 1, pp. 97-136, Jan. 1980.
25. D. Gao, V. MahadeVan, and N. Vasconcelos, "The discriminant centersurround hypothesis for bottom-up saliency," in Proc. Adv. Neural Inf. Process. Syst., 2007, pp. 497-504.
26. Y. Hu, D. Rajan, and L.-T. Chia, "Adaptive local context suppression of multiple cues for salient visual attention detection," in Proc. IEEE Int. Conf. Multimedia Expo., 2005, pp. 346-349. (Pubitemid 44668875)
27. T. Kadir and M. Brady, "Saliency, scale and image description," Int. J. Comput. Vis., vol. 45, no. 2, pp. 83-105, Nov. 2001. (Pubitemid 33125858)
28. V. Gopalakrishnan, Y. Hu, and D. Rajan, "Random walks on graphs to model saliency in images," in Proc. IEEE Conf. Comput. Vis. Pattern Recogn., 2009, pp. 1698-1705.
29. Z. Yu and H.-S.Wong, "A rule based technique for extraction of visual attention regions based on real-time clustering," IEEE Trans. Multimedia, vol. 9, no. 4, pp. 766-784, Jun. 2007. (Pubitemid 46808604)
30. H. Liu, S. Jiang, Q. Huang, C. Xu, and W. Gao, "Region-based visual attention analysis with its application in image browsing on small displays," in Proc. ACM Int. Conf. Multimedia, 2007, pp. 305-308.
31. M. Z. Aziz and B. Mertsching, "Fast and robust generation of feature maps for region-based visual attention," IEEE Trans. Image Process., vol. 17, no. 5, pp. 633-644, May 2008. (Pubitemid 351597156)
32. Y. Hu, D. Rajan, and L.-T. Chia, "Scale adaptive visual attention detection by subspace analysis," in Proc. ACM Int. Conf. Multimedia, 2007, pp. 525-528.
33. C. Guo, Q. Ma, and L. Zhang, "Spatio-temporal saliency detection using phase spectrum of quaternion Fourier transform," in Proc. IEEE Conf. Comput. Vis. Pattern Recogn., 2008, pp. 1-8.
34. N. D. Bruce and J. K. Tsotsos, "Saliency based on information maximization," in Proc. Adv. Neural Inf. Process. Syst., 2006, pp. 155-162.
35. M. Cerf, J. Harel, W. Einhauser, and C. Koch, "Predicting human gaze using low-level saliency combined with face detection," in Proc. Adv. Neural Inf. Process. Syst., 2008, pp. 241-248.
36. G. Abdollahian and E. J. Delp, "Finding regions of interest in home videos based on camera motion," in Proc. IEEE Int. Conf. Image Process, 2007, pp. 545-548.
37. X. Qiu, S. Jiang, H. Liu, Q. Huang, and L. Cao, "Spatial-temporal attention analysis for home video," in Proc. IEEE Int. Conf. Multimedia Expo., 2008, pp. 1517-1520.
38. W. Kienzle, F. A. Wichmann, B. Scholkopf, and M. O. Franz, "A nonparametric approach to bottom-up visual saliency," in Proc. Adv. Neural Inf. Process. Syst., 2007, pp. 689-696.
39. T. Liu, N. Zheng, W. Ding, and Z. Yuan, "Video attention: Learning to detect a salient object sequence," in Proc. Int. Conf. Pattern Recogn., 2008, pp. 1-4.
40. J. Li, Y. Tian, T. Huang, and W. Gao, "Probabilistic multi-task learning for visual saliency estimation in video," Int. J. Comput. Vis., vol. 90, no. 2, pp. 150-165, Nov. 2010.
41. F. Li and C. Sminchisescu, "Convex multiple instance learning by estimating likelihood ratio," in Proc. Adv. Neural Inf. Process. Syst., Dec. 2010, pp. 1360-1368.
42. C. Bergeron, J. Zaretzki, C. Breneman, and K. P. Bennett, "Multiple-instance ranking," in Proc. 25th Int. Conf. Mach. Learn., 2008, pp. 48-55.
43. Y. Hu, M. Li, and N. Yu, "Multiple instance ranking: Learning to rank images for image retrieval," in Proc. IEEE Conf. Comput. Vis. Pattern Recogn., 2008, pp. 1-8.
44. P.-H. Tseng, R. Carmi, I. G. M. Cameron, D. P. Munoz, and L. Itti, "Quantifying center bias of observers in free viewing of dynamic natural scenes," J. Vis., vol. 9, no. 7, pp. 1-16, Jul. 2009.
45. B. J. Frey and D. Dueck, "Clustering by passing messages between data points," Science, vol. 315, no. 5814, pp. 972-976, Feb. 2007. (Pubitemid 46281181)
46. A. Dempster, N. Laird, and D. Rubin, "Maximum likelihood from incomplete data via the em algorithm," J. Roy. Stat. Soc., Ser. B, vol. 39, no. 1, pp. 1-38, 1977.
47. T. Joachims, "Training linear SVMs in linear time," in Proc. ACM Conf. Knowl. Discovery Data Mining, 2006, pp. 217-226. (Pubitemid 44535518)
48. R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin, "Liblinear: A library for large linear classification," J. Mach. Learn. Res., vol. 9, no. 9, pp. 1871-1874, 2008.
49. Q. Zhao and C. Koch, "Learning a saliency map using fixated locations in natural scenes," J. Vis., vol. 11, no. 3, pp. 1-15, 2011.