Discriminative saliency propagation with sink points

Pattern Recognition

Volumn 60, Issue , 2016, Pages 2-12

  Chen, Shuhan ^a,b   Zheng, Ling ^a   Hu, Xuelong ^a   Zhou, Ping ^b  

^a YANGZHOU UNIVERSITY   (China)

^b Wanfang Electronic Technology Co Ltd   (China)

Author keywords

Saliency Detection; Saliency Propagation; Similarity Metric; Sink Points

Indexed keywords

IMAGE SEGMENTATION;

CLUTTERED BACKGROUNDS; COMPUTATIONAL OVERHEADS; PROPAGATION MECHANISM; REFINEMENT TECHNIQUES; SALIENCY DETECTION; SALIENT OBJECT DETECTION; SIMILARITY METRICS; SINK POINTS;

OBJECT DETECTION;

EID: 84994805560     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2016.05.016     Document Type: Article

References (48)

1. [1] Jiang, P., Ling, H., Yu, J., Peng, J., Salient region detection by UFO: uniqueness, focusness and objectness. ICCV, 2013, 1976–1983.
2. [2] Borji, A., Cheng, M.-M., Jiang, H., Li, J., Salient object detection: a survey. arXiv, 2014.
3. [3] Hou, X., Zhang, L., Saliency detection: a spectral residual approach. CVPR, 2007, 1–8.
4. [4] Cheng, M.-M., Zhang, G.-X., Mitra, N.J., Huang, X., Hu, S.-M., Global contrast based salient region detection. CVPR, 2011, 569–582.
5. [5] Cheng, M.-M., Zhang, Z., Lin, W.-Y., Torr, P., BING: binarized normed gradients for objectness estimation at 300 fps. CVPR, 2014, 3286–3293.
6. [6] Rutishauser, U., Walther, D., Koch, C., Perona, P., Is bottom-up attention useful for object recognition. CVPR, 2004, II-37–II-44.
7. [7] Li, X., Li, Y., Shen, C., Dick, A., Hengel, A., Contextual hypergraph modeling for salient object detection. ICCV, 2013, 3328–3335.
8. [8] Mahadevan, V., Vasconcelos, N., Saliency-based discriminant tracking. CVPR, 2009, 1007–1013.
9. [9] Itti, L., Automatic foveation for video compression using a neurobiological model of visual attention. IEEE Trans. Image Process. 13:10 (2004), 1304–1318.
10. [10] Wei, Y., Wen, F., Zhu, W., Sun, J., Geodesic saliency using background priors. ECCV, 2012, 29–42.
11. [11] Jiang, H., Wang, J., Yuan, Z., Wu, Y., Zheng, N., Li, S., Salient object detection: a discriminative regional feature integration approach. CVPR, 2013, 2083–2090.
12. [12] Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.-H., Saliency detection via graph-based manifold ranking. CVPR, 2013, 3166–3173.
13. [13] Li, X., Lu, H., Zhang, L., Ruan, X., Yang, M.-H., Saliency detection via dense and sparse reconstruction. ICCV, 2013, 2976–2983.
14. [14] Jiang, B., Zhang, L., Lu, H., Yang, C., Yang, M.-H., Saliency detection via absorbing Markov chain. ICCV, 2013, 1665–1672.
15. [15] Perazzi, F., Krahenbuhl, P., Pritch, Y., Hornung, A., Saliency filters: contrast based filtering for salient region detection. CVPR, 2012, 733–740.
16. [16] Yan, Q., Xu, L., Shi, J., Jia, J., Hierarchical saliency detection. CVPR, 2013, 1155–1162.
17. [17] Cheng, M.-M., Warrell, J., Lin, W.-Y., Zheng, S., Vineet, V., Crook, N., Efficient salient region detection with soft image abstraction. ICCV, 2013, 1529–1536.
18. [18] Liu, T., Sun, J., Zheng, N., Tang, X., Shum, H.-Y., Learning to detect a salient object. CVPR, 2007, 1–8.
19. [19] Li, N., Ye, J., Ji, Y., Ling, H., Yu, J., Saliency detection on light fields. CVPR, 2014, 2806–2813.
20. [20] Li, H., Lu, H., Lin, Z., Shen, X., Price, B., Inner and inter label propagation: salient object detection in the wild. IEEE Trans. Image Process. 24:10 (2015), 3176–3186.
21. [21] Lu, S., Mahadevan, V., Vasconcelos, N., Learning optimal seeds for diffusion-based salient object detection. CVPR, 2014, 2790–2797.
22. [22] Gopalakrishnan, V., Hu, Y., Rajan, D., Random walks on graphs for salient object detection in images. IEEE Trans. Image Process. 19:12 (2010), 3232–3242.
23. [23] Sun, J., Lu, H., Liu, X., Saliency region detection based on Markov absorption probabilities. IEEE Trans. Image Process. 24:5 (2015), 1639–1649.
24. [24] Li, C., Yuan, Y., Cai, W., Xia, Y., Feng, D.D., Robust saliency detection via regularized random walks ranking. CVPR, 2015.
25. [25] Gong, C., Tao, D., Liu, W., Maybank, S.J., Fang, M., Fu, K., Yang, J., Saliency propagation from simple to difficult. CVPR, 2015.
26. [26] Frintrop, S., Werner, T., Garcia, G.M., Traditional saliency reloaded: a good old model in new shape. CVPR, 2015.
27. [27] Wang, L., Lu, H., Ruan, X., Yang, M.-H., Deep networks for saliency detection via local estimation and global search. CVPR, 2015.
28. [28] Donoser, M., Bischof, H., Diffusion processes for retrieval revisited. CVPR, 2013, 1320–1327.
29. [29] Felzenszwalb, P.F., Huttenlocher, D.P., Efficient graph-based image segmentation. Int. J. Comput. Vis. 59:2 (2004), 167–181.
30. [30] Wang, J., Lu, H., Li, X., Tong, N., Liu, W., Saliency detection via background and foreground seed selection. Neurocomputing 152:25 (2015), 359–368.
31. [31] Qin, Y., Lu, H., Xu, Y., Wang, H., Saliency detection via cellular automata. CVPR, 2015.
32. [32] R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, S. Susstrunk, Slic superpixels, Technical report, 2010.
33. [33] Cheng, X.-Q., Du, P., Guo, J., Zhu, X., Chen, Y., Ranking on data manifold with sink points. IEEE Trans. Knowl. Data Eng. 25:1 (2013), 177–191.
34. [34] Zhu, L., Klein, D.A., Frintrop, S., Cao, Z., Cremers, A.B., A multisize superpixel approach for salient object detection based on multivariate normal distribution estimation. IEEE Trans. Image Process. 23:12 (2014), 5094–5107.
35. [35] Li, S., Lu, H., Lin, Z., Shen, X., Price, B., Adaptive metric learning for saliency detection. IEEE Trans. Image Process. 24:11 (2015), 3321–3331.
36. [36] Zhang, L., Zhao, S., Liu, W., Lu, H., Saliency detection via sparse reconstruction and joint label inference in multiple features. Neurocomputing 155:1 (2015), 1–11.
37. [37] Otsu, N., A threshold selection method from gray level histograms. IEEE Trans. Syst. Man Cybern. 9:1 (1979), 62–66.
38. [38] Achanta, R., Hemami, S., Estrada, F., Susstrunk, S., Frequency-tuned salient region detection. CVPR, 2009, 1597–1604.
39. [39] Cheng, M.-M., Mitra, N.J., Huang, X., Torr, P.H.S., Hu, S.-M., Global contrast based salient region detection. IEEE Trans. Pattern Anal. Mach. Intell 37:3 (2015), 569–582.
40. [40] Martin, D., Fowlkes, C., Tal, D., Malik, J., A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. ICCV, 2001, 416–423.
41. [41] Zhu, W., Liang, S., Wei, Y., Sun, J., Saliency optimization from robust background detection. CVPR, 2014, 2814–2821.
42. [42] Li, Y., Hou, X., Koch, C., Rehg, J., Yuille, A., The secrets of salient object segmentation. CVPR, 2014, 280–287.
43. [43] Paris, S., Durand, F., A fast approximation of the bilateral filter using a signal processing approach. Int. J. Comput. Vis. 81:1 (2009), 24–52.
44. [44] Tong, N., Lu, H., Zhang, Y., Ruan, X., Salient object detection via global and local cues. Pattern Recognit. 48:10 (2015), 3258–3267.
45. [45] Tong, N., Lu, H., Yang, M., Salient object detection via bootstrap learning. CVPR, 2015.
46. [46] Borji, A., Cheng, M.-M., Jiang, H., Li, J., Salient object detection: a benchmark. IEEE Trans. Image Process. 24:12 (2015), 5706–5722.
47. [47] Zhang, X., Xu, C., Li, M., Teng, R.K.F., Study of visual saliency detection via nonlocal anisotropic diffusion equation. Pattern Recognit. 48:4 (2015), 1315–1327.
48. [48] Wei, Y., Liang, X., Chen, Y., Shen, X., Cheng, M.-M., Zhao, Y., Yan, S., STC: a simple to complex framework for weakly-supervised semantic segmentation. arXiv, 2015.