Background Prior-Based Salient Object Detection via Deep Reconstruction Residual

IEEE Transactions on Circuits and Systems for Video Technology

Volumn 25, Issue 8, 2015, Pages 1309-1321

  Han, Junwei ^a   Zhang, Dingwen ^a   Hu, Xintao ^a   Guo, Lei ^a   Ren, Jinchang ^b   Wu, Feng ^c  

^a NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

^b UNIVERSITY OF STRATHCLYDE   (United Kingdom)

^c UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

deep reconstruction residual; salient object detection; stacked denoising autoencoder

Indexed keywords

LEARNING SYSTEMS; OBJECT RECOGNITION;

AUTO ENCODERS; COMPREHENSIVE EVALUATION; CONTENT-BASED MULTIMEDIA; CONVENTIONAL APPROACH; DEEP RECONSTRUCTION RESIDUAL; RESEARCH INTERESTS; SALIENT OBJECT DETECTION; STATE-OF-THE-ART ALGORITHMS;

OBJECT DETECTION;

EID: 84926497888     PISSN: 10518215     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCSVT.2014.2381471     Document Type: Article

References (51)

1. J. Sun and H. Ling, "Scale and object aware image retargeting for thumbnail browsing," in Proc. IEEE Int. Conf. Comput. Vis., Nov. 2011, pp. 1511-1518.
2. T. Chen, M.-M. Cheng, P. Tan, A. Shamir, and S.-M. Hu, "Sketch2Photo: Internet image montage," ACM Trans. Graph., vol. 28, no. 5, Dec. 2009, Art. ID 124.
3. X. Zhen, L. Shao, and X. Li, "Action recognition by spatio-temporal oriented energies," Inf. Sci., vol. 281, pp. 295-309, Oct. 2014.
4. 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.
5. 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.
6. T. Judd, K. Ehinger, F. Durand, and A. Torralba, "Learning to predict where humans look," in Proc. IEEE 12th Int. Conf. Comput. Vis., Sep./Oct. 2009, pp. 2106-2113.
7. A. Borji, "Boosting bottom-up and top-down visual features for saliency estimation," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2012, pp. 438-445.
8. L. Zhang, M. H. Tong, T. K. Marks, H. Shan, and G. W. Cottrell, "SUN: A Bayesian framework for saliency using natural statistics," J. Vis., vol. 8, no. 7, Dec. 2008, Art. ID 32.
9. J. Han, S. He, X. Qian, D. Wang, L. Guo, and 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.
10. X. Hou and L. Zhang, "Dynamic visual attention: Searching for coding length increments," in Advances in Neural Information Processing Systems. Red Hook, NY, USA: Curran & Associates Inc., 2008, pp. 681-688.
11. X. Shen and Y. Wu, "A unified approach to salient object detection via low rank matrix recovery," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2012, pp. 853-860.
12. B. Han, H. Zhu, and Y. Ding, "Bottom-up saliency based on weighted sparse coding residual," in Proc. ACM Int. Conf. Multimedia, 2011, pp. 1117-1120.
13. 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.
14. H. J. Seo and P. Milanfar, "Static and space-time visual saliency detection by self-resemblance," J. Vis., vol. 9, no. 12, Nov. 2009, Art. ID 15.
15. X. Hou and L. Zhang, "Saliency detection: A spectral residual approach," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2007, pp. 1-8.
16. R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk, "Frequency-tuned salient region detection," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2009, pp. 1597-1604.
17. 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 Recognit., Jun. 2011, pp. 409-416.
18. Y. Wei, F. Wen, W. Zhu, and J. Sun, "Geodesic saliency using background priors," in Proc. 12th Eur. Conf. Comput. Vis., 2012, pp. 29-42.
19. C. Yang, L. Zhang, H. Lu, X. Ruan, and M.-H. Yang, "Saliency detection via graph-based manifold ranking," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2013, pp. 3166-3173.
20. Y. Bengio and Y. LeCun, "Scaling learning algorithms towards AI," in Large-Scale Kernel Machines, L. Bottou, O. Chapelle, D. DeCoste, and J. Weston, Eds. Cambridge, MA, USA: MIT Press, 2007.
21. D. Erhan, Y. Bengio, A. Courville, P.-A. Manzagol, P. Vincent, and S. Bengio, "Why does unsupervised pre-training help deep learning?" J. Mach. Learn. Res., vol. 11, pp. 625-660, Feb. 2010.
22. L. Shao, D. Wu, and 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.
23. G. E. Hinton and R. R. Salakhutdinov, "Reducing the dimensionality of data with neural networks," Science, vol. 313, no. 5786, pp. 504-507, Jul. 2006.
24. Y. Bengio, A. Courville, and P. Vincent, "Representation learning: A review and new perspectives," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 8, pp. 1798-1828, Aug. 2013.
25. P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol, "Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion," J. Mach. Learn. Res., vol. 11, pp. 3371-3408, Jan. 2010.
26. A. Ng, "Sparse autoencoder," CS294A Lecture Notes, Stanford Univ., Stanford, CA, USA, Tech. Rep., 2011, p. 72.
27. H. C. Shin, M. R. Orton, D. J. Collins, S. J. Doran, and M. O. Leach, "Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 8, pp. 1930-1943, Aug. 2013.
28. W. Kim and C. Kim, "Spatiotemporal saliency detection using textural contrast and its applications," IEEE Trans. Circuits Syst. Video Technol., vol. 24, no. 4, pp. 646-659, Apr. 2014.
29. X. Qian, J. Han, G. Cheng, and L. Guo, "Optimal contrast based saliency detection," Pattern Recognit. Lett., vol. 34, no. 11, pp. 1270-1278, Aug. 2013.
30. Y. Bengio, "Practical recommendations for gradient-based training of deep architectures," in Neural Networks: Tricks Trade, K.-R. Müller, G. Montavon, and G. B. Orr, Eds. Berlin, Germany: Springer-Verlag, 2013.
31. I. J. Goodfellow, D. Warde-Farley, M. Mirza, A. Courville, and Y. Bengio, "Maxout networks," in Proc. IEEE Int. Conf. Mach. Learn., 2013, pp. 1319-1327.
32. N. Wang, J. Melchior, and L. Wiskott, "An analysis of Gaussian-binary restricted Boltzmann machines for natural images," in Proc. ESANN, 2012, pp. 287-292.
33. S. Goferman, L. Zelnik-Manor, and A. Tal, "Context-Aware saliency detection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 10, pp. 1915-1926, Oct. 2012.
34. R. Margolin, A. Tal, and L. Zelnik-Manor, "What makes a patch distinct?" in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2013, pp. 1139-1146.
35. P. Siva, C. Russell, T. Xiang, and L. Agapito, "Looking beyond the image: Unsupervised learning for object saliency and detection," in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., Jun. 2013, pp. 3238-3245.
36. P. F. Felzenszwalb and D. P. Huttenlocher, "Efficient graph-based image segmentation," Int. J. Comput. Vis., vol. 59, no. 2, pp. 167-181, Sep. 2004.
37. K.-Y. Chang, T.-L. Liu, H.-T. Chen, and S.-H. Lai, "Fusing generic objectness and visual saliency for salient object detection," in Proc. IEEE Int. Conf. Comput. Vis., Nov. 2011, pp. 914-921.
38. H. Jiang, J. Wang, Z. Yuan, T. Liu, N. Zheng, and S. Li, "Automatic salient object segmentation based on context and shape prior," in Proc. Brit. Mach. Vis. Conf., 2011, p. 7.
39. Y. Xie, H. Lu, and M.-H. Yang, "Bayesian saliency via low and mid level cues," IEEE Trans. Image Process., vol. 22, no. 5, pp. 1689-1698, May 2013.
40. V. Movahedi and J. H. Elder, "Design and perceptual validation of performance measures for salient object segmentation," in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Workshops, Jun. 2010, pp. 49-56.
41. A. Borji, D. N. Sihite, and L. Itti, "Salient object detection: A benchmark," in Proc. 12th Eur. Conf. Comput. Vis., 2012, pp. 414-429.
42. J. Harel, C. Koch, and P. Perona, "Graph-based visual saliency," in Advances in Neural Information Processing Systems. Red Hook, NY, USA: Curran & Associates Inc., 2006, pp. 545-552.
43. X. Hou and L. Zhang, "Dynamic visual attention: Searching for coding length increments," in Advances in Neural Information Processing Systems. Red Hook, NY, USA: Curran & Associates Inc., 2008, pp. 681-688.
44. Y. Jia and M. Han, "Category-independent object-level saliency detection," in Proc. IEEE Int. Conf. Comput. Vis., Dec. 2013, pp. 1761-1768.
45. Q. Wang, Y. Yuan, P. Yan, and X. Li, "Saliency detection by multipleinstance learning," IEEE Trans. Cybern., vol. 43, no. 2, pp. 660-672, Apr. 2013.
46. D. R. Martin, C. C. Fowlkes, and J. Malik, "Learning to detect natural image boundaries using local brightness, color, and texture cues," IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 5, pp. 530-549, May 2004.
47. L. Sun and T. Shibata, "Unsupervised object extraction by contour delineation and texture discrimination based on oriented edge features," IEEE Trans. Circuits Syst. Video Technol., vol. 24, no. 5, pp. 780-788, May 2014.
48. C. Lang, G. Liu, J. Yu, and S. Yan, "Saliency detection by multitask sparsity pursuit," IEEE Trans. Image Process., vol. 21, no. 3, pp. 1327-1338, Mar. 2012.
49. Y. Li, Y. Zhou, L. Xu, X. Yang, and J. Yang, "Incremental sparse saliency detection," in Proc. 16th IEEE Int. Conf. Image Process., Nov. 2009, pp. 3093-3096.
50. S. Alpert, M. Galun, R. Basri, and A. Brandt, "Image segmentation by probabilistic bottom-up aggregation and cue integration," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2007, pp. 1-8.
51. R. Margolin, L. Zelnik-Manor, and A. Tal, "How to evaluate foreground maps?" in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2014, pp. 1-8.