Manifold Ranking-Based Matrix Factorization for Saliency Detection

IEEE Transactions on Neural Networks and Learning Systems

Volumn 27, Issue 6, 2016, Pages 1122-1134

  Tao, Dapeng ^a   Cheng, Jun ^b,c   Song, Mingli ^d   Lin, Xu ^b,e  

^a YUNNAN UNIVERSITY   (China)

^b SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY   (China)

^c CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^d ZHEJIANG UNIVERSITY   (China)

^e CHINA THREE GORGES UNIVERSITY   (China)

Author keywords

Manifold ranking (MR); matrix factorization; optimization algorithm; Saliency detection

Indexed keywords

ALGORITHMS; FACTORIZATION; IMAGE MATCHING; OBJECT RECOGNITION; OPTIMIZATION; PIXELS;

FEATURE INFORMATION; IMAGE QUALITY ASSESSMENT; MANIFOLD RANKING; MATRIX FACTORIZATIONS; OPTIMIZATION ALGORITHMS; SALIENCY DETECTION; SPATIAL INFORMATIONS; STATE OF THE ART;

MATRIX ALGEBRA;

EID: 84939132379     PISSN: 2162237X     EISSN: 21622388     Source Type: Journal    
DOI: 10.1109/TNNLS.2015.2461554     Document Type: Article

References (71)

1. Q. Ma, L. Zhang, B. Wang, "New strategy for image and video quality assessment," J. Electron. Imag., vol. 19, no. 1, pp. 011019-1-011019-14, 2010.
2. A. Ninassi, O. Le Meur, P. Le Callet, D. Barbba, "Does where you gaze on an image affect your perception of quality? Applying visual attention to image quality metric," in Proc. Int. Conf. Image Process., vol. 2. San Antonio, TX, USA, Sep. 2007, pp. 169-172.
3. D. Gao and N. Vasconcelos, "Discriminant saliency for visual recognition from cluttered scenes," in Proc. Adv. Neural Inf. Process. Syst., Vancouver, BC, Canada, Dec. 2004, pp. 481-488.
4. C. Siagian and L. Itti, "Biologically inspired mobile robot vision localization," IEEE Trans. Robot., vol. 25, no. 4, pp. 861-873, Aug. 2009.
5. D. Walther and C. Koch, "Modeling attention to salient proto-objects," Neural Netw., vol. 19, no. 9, pp. 1395-1407, 2006.
6. S. Frintrop and P. Jensfelt, "Attentional landmarks and active gaze control for visual SLAM," IEEE Trans. Robot., vol. 24, no. 5, pp. 1054-1065, Oct. 2008.
7. J. Han, L. Shao, D. Xu, J. Shotton, "Enhanced computer vision with Microsoft Kinect sensor: A review," IEEE Trans. Cybern., vol. 43, no. 5, pp. 1318-1334, Oct. 2013.
8. J. Han, D. Zhang, G. Cheng, L. Guo, J. Ren, "Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 6, pp. 3325-3337, Jun. 2015.
9. F. Zhu and L. Shao, "Weakly-supervised cross-domain dictionary learning for visual recognition," Int. J. Comput. Vis., vol. 109, nos. 1-2, pp. 42-59, Aug. 2014.
10. J. Han, S. He, X. Qian, D. Wang, L. Guo, T. Liu, "An objectoriented visual saliency detection framework based on sparse coding representations," IEEE Trans. Circuits Syst. Video Technol., vol. 23, no. 12, pp. 2009-2021, Dec. 2013.
11. D. Tao, X. Lin, L. Jin, X. Li, "Principal component 2-dimensional long short-term memory for font recognition on single chinese characters," IEEE Trans. Cybern., Mar. 2015, doi: 10.1109/TCYB.2015.2414920.
12. N. Sadaka and L. J. Karam, "Efficient perceptual attentive superresolution," in Proc. Int. Conf. Image Process., Cairo, Egypt, Nov. 2009, pp. 3113-3116.
13. L. I. Kuncheva and W. J. Faithfull, "PCA feature extraction for change detection in multidimensional unlabeled data," IEEE Trans. Neural Netw. Learn. Syst., vol. 25, no. 1, pp. 69-80, Jan. 2014.
14. V. Mahadevan and N. Vasconcelos, "Spatiotemporal saliency in dynamic scenes," IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 1, pp. 171-177, Jan. 2010.
15. S. Mitri, S. Frintrop, K. Pervolz, H. Surmann, A. Nuchter, "Robust object detection at regions of interest with an application in ball recognition," in Proc. Int. Conf. Robot. Antom., Barcelona, Spain, Apr. 2005, pp. 125-130.
16. X. Xu, I. W. Tsang, D. Xu, "Soft margin multiple kernel learning," IEEE Trans. Neural Netw. Learn. Syst., vol. 24, no. 5, pp. 749-761, May 2013.
17. J. Han et al., "Representing and retrieving video shots in human-centric brain imaging space," IEEE Trans. Image Process., vol. 22, no. 7, pp. 2723-2736, Jul. 2013.
18. J. Han, D. Wang, L. Shao, X. Qian, G. Cheng, J. Han, "Image visual attention computation and application via the learning of object attributes," Mach. Vis. Appl., vol. 25, no. 7, pp. 1671-1683, 2014.
19. T. Kadir and M. Brady, "Saliency, scale and image description," Int. J. Comput. Vis., vol. 45, no. 2, pp. 83-105, 2001.
20. J. Li, D. Xu, W. Gao, "Removing label ambiguity in learning-based visual saliency estimation," IEEE Trans. Image Process., vol. 21, no. 4, pp. 1513-1525, Apr. 2012.
21. H. E. Egeth and S. Yantis, "VISUAL ATTENTION: Control, representation, time course," Annu. Rev. Psychol., vol. 48, no. 1, pp. 269-297, 1997.
22. L. Itti, G. Rees, J. K. Tsotsos, Eds., Neurobiology of Attention. San Diego, CA, USA: Elsevier, 2005.
23. J. Han, D. Zhang, X. Hu, L. Guo, J. Ren, F. Wu, "Background prior based salient object detection via deep reconstruction residual," IEEE Trans. Circuits Syst. Video Technol., vol. 25, no. 8, pp. 1309-1321, Aug. 2015.
24. D. Gao and N. Vasconcelos, "Bottom-up saliency is a discriminant process," in Proc. IEEE Int. Conf. Comput. Vis., Rio de Janeiro, Brazil, Oct. 2007, pp. 1-6.
25. D. Gao and N. Vasconcelos, "Integrated learning of saliency, complex features, object detectors from cluttered scenes," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., San Diego, CA, USA, Jun. 2005, pp. 282-287.
26. Y. Wei, F. Wen, W. Zhu, J. Sun, "Geodesic saliency using background priors," in Proc. Eur. Conf. Comput. Vis., Florence, Italy, Oct. 2012, pp. 29-42.
27. C. Yang, L. Zhang, H. Lu, X. Ruan, M.-H. Yang, "Saliency detection via graph-based manifold ranking," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Portland, OR, USA, Jun. 2013, pp. 3166-3173.
28. J. Yu, Y. Rui, D. Tao, "Click prediction for Web image reranking using multimodal sparse coding," IEEE Trans. Image Process., vol. 23, no. 5, pp. 2019-2032, May 2014.
29. J. Yu, Y. Rui, B. Chen, "Exploiting click constraints and multi-view features for image re-ranking," IEEE Trans. Multimedia, vol. 16, no. 1, pp. 159-168, Jan. 2014.
30. J. Yu, M. Wang, D. Tao, "Semisupervised multiview distance metric learning for cartoon synthesis," IEEE Trans. Image Process., vol. 21, no. 11, pp. 4636-4648, Nov. 2012.
31. J. Yu, D. Tao, M. Wang, Y. Rui, "Learning to rank using user clicks and visual features for image retrieval," IEEE Trans. Cybern., vol. 45, no. 4, pp. 767-779, Apr. 2015.
32. Y. Hu, D. Zhang, J. Ye, X. Li, X. He, "Fast and accurate matrix completion via truncated nuclear norm regularization," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 9, pp. 2117-2130, Sep. 2013.
33. S. Yang, Z. Yi, M. Ye, X. He, "Convergence analysis of graph regularized non-negative matrix factorization," IEEE Trans. Knowl. Data Eng., vol. 26, no. 9, pp. 2151-2165, Sep. 2014.
34. D. Cai and X. He, "Manifold adaptive experimental design for text categorization," IEEE Trans. Knowl. Data Eng., vol. 24, no. 4, pp. 707-719, Apr. 2012.
35. Y. Nesterov, "A method of solving a convex programming problem with convergence rate O(1/k2)," Soviet Math. Doklady, vol. 27, no. 2, pp. 372-376, 1983.
36. L. Itti, C. Koch, 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.
37. G. Kootstra, A. Nederveen, B. de Boer, "Paying attention to symmetry," in Proc. Brit. Mach. Vis. Conf., Sep. 2008, pp. 1115-1125.
38. O. Le Meur, P. Le Callet, D. Barba, D. Thoreau, "A coherent computational approach to model bottom-up visual attention," IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 5, pp. 802-817, May 2006.
39. O. Le Meur, P. Le Callet, D. Barba, "Predicting visual fixations on video based on low-level visual features," Vis. Res., vol. 47, no. 19, pp. 2483-2498, 2007.
40. N. D. B. Bruce and J. K. Tsotsos, "Saliency based on information maximization," in Proc. Adv. Neural Inf. Process. Syst., Vancouver, BC, Canada, Jun. 2005, pp. 155-162.
41. X. Hou and L. Zhang, "Dynamic visual attention: Searching for coding length increments," in Proc. Adv. Neural Inf. Process. Syst., Vancouver, BC, Canada, Dec. 2008, pp. 681-688.
42. M. Mancas, "Computational attention: Modelisation and application to audio and image processing," Ph.D. dissertation, Numediart Inst. Creative Technol., Univ. Mons, Mons, Belgium, 2007.
43. A. Oliva, A. Torralba, M. S. Castelhano, J. M. Henderson, "Topdown control of visual attention in object detection," in Proc. Int. Conf. Image Process., Barcelona, Spain, Sep. 2003, pp. I-253-I-256.
44. A. Torralba, "Modeling global scene factors in attention," J. Opt. Soc. Amer., vol. 20, no. 7, pp. 1407-1418, 2003.
45. L. Zhang, M. H. Tong, T. K. Marks, H. Shan, G. W. Cottrell, "SUN: A Bayesian framework for saliency using natural statistics," J. Vis., vol. 8, no. 7, pp. 1-20, 2008.
46. R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, S. Süsstrunk, "SLIC superpixels compared to state-of-the-art superpixel methods," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 11, pp. 2274-2282, Nov. 2012.
47. T. Avraham and M. Lindenbaum, "Esaliency (extended saliency): Meaningful attention using stochastic image modeling," IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 4, pp. 693-708, Apr. 2010.
48. J. Harel, C. Koch, P. Perona, "Graph-based visual saliency," in Proc. Adv. Neural Inf. Process. Syst., Vancouver, BC, Canada, Dec. 2006, pp. 545-552.
49. C. Guo, Q. Ma, L. Zhang, "Spatio-temporal saliency detection using phase spectrum of quaternion Fourier transform," in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., Anchorage, AK, USA, Jun. 2008, pp. 1-8.
50. C. Guo and L. Zhang, "A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression," IEEE Trans. Image Process., vol. 19, no. 1, pp. 185-198, Jan. 2010.
51. X. Hou and L. Zhang, "Saliency detection: A spectral residual approach," in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., Minneapolis, MN, USA, Jun. 2007, pp. 1-8.
52. R. Achanta, S. Hemami, F. J. Estrada, S. Süsstrunk, "Frequencytuned salient region detection," in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., Miami, FL, USA, Jun. 2009, pp. 1597-1604.
53. T. Judd, K. Ehinger, F. Durand, A. Torralba, "Learning to predict where humans look," in Proc. IEEE Int. Conf. Comput. Vis., Kyoto, Japan, Sep. 2009, pp. 2106-2113.
54. W. Kienzle, M. O. Franz, B. Scholkopf, F. A. Wichmann, "Centersurround patterns emerge as optimal predictors for human saccade targets," J. Vis., vol. 9, pp. 1-15, May 2009.
55. A. M. Treisman and G. Gelade, "A feature-integration theory of attention," Cognit. Psychol., vol. 12, no. 1, pp. 97-136, 1980.
56. C. Koch and S. Ullman, "Shifts in selective visual attention: Towards the underlying neural circuitry," Human Neurobiol., vol. 4, no. 4, pp. 219-227, 1985.
57. L. Itti, "Automatic foveation for video compression using a neurobiological model of visual attention," IEEE Trans. Image Process., vol. 13, no. 10, pp. 1304-1318, Oct. 2004.
58. H. J. Seo and P. Milanfar, "Static and space-time visual saliency detection by self-resemblance," J. Vis., vol. 9, no. 12, pp. 1-27, 2009.
59. V. Bloom, I. Griva, B. Kwon, A.-R. Wolff, "Exterior-point method for support vector machines," IEEE Trans. Neural Netw. Learn. Syst., vol. 25, no. 7, pp. 1390-1393, Jul. 2014.
60. R. J. Peters and L. Itti, "Beyond bottom-up: Incorporating taskdependent influences into a computational model of spatial attention," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Minneapolis, MN, USA, Jun. 2007, pp. 1-8.
61. L. Shao, D. Wu, X. Li, "Learning deep and wide: A spectral method for learning deep networks," IEEE Trans. Neural Netw. Learn. Syst., vol. 25, no. 12, pp. 2303-2308, Dec. 2014.
62. J. Li, Y. Tian, T. Huang, W. Gao, "Probabilistic multi-task learning for visual saliency estimation in video," Int. J. Comput. Vis., vol. 90, no. 2, pp. 150-165, 2010.
63. D. P. Bertsekas, Nonlinear Programming, 2nd ed. Belmont, MA, USA: Athena Scientific, 1999.
64. N. Guan, D. Tao, Z. Luo, B. Yuan, "NeNMF: An optimal gradient method for nonnegative matrix factorization," IEEE Trans. Signal Process., vol. 60, no. 6, pp. 2882-2898, Jun. 2012.
65. P. L. Bartlett and S. Mendelson, "Rademacher and Gaussian complexities: Risk bounds and structural results," J. Mach. Learn. Res., vol. 3, pp. 463-482, Mar. 2003.
66. M. Ledoux and M. Talagrand, Probability in Banach Spaces: Isoperimetry and Processes. New York, NY, USA: Springer-Verlag, 1991.
67. Q. Yan, L. Xu, J. Shi, J. Jia, "Hierarchical saliency detection," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Portland, OR, USA, Jun. 2013, pp. 1155-1162.
68. Q. Yan, J. Shi, L. Xu, J. Jia. (Aug. 2014). "Hierarchical saliency detection on extended CSSD." [Online]. Available: http://arxiv. org/abs/1408.5418
69. T. Liu et al., "Learning to detect a salient object," IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 2, pp. 353-367, Feb. 2011.
70. J. Zhang and S. Sclaroff, "Saliency detection: A Boolean map approach," in Proc. IEEE Int. Conf. Comput. Vis., Sydney, Australia, Dec. 2013, pp. 153-160.
71. M.-M. Cheng, G.-X. Zhang, N. J. Mitra, X. Huang, S.-M. Hu, "Global contrast based salient region detection," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Providence, RI, USA, Jun. 2011, pp. 409-416.