Deep contrast learning for salient object detection

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

Volumn 2016-December, Issue , 2016, Pages 478-487

  Li, Guanbin ^a   Yu, Yizhou ^a  

^a UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; CONVOLUTION; IMAGE SEGMENTATION; NEURAL NETWORKS; OBJECT DETECTION; OBJECT RECOGNITION; PIXELS;

CONTOUR LOCALIZATION; CONVOLUTIONAL NEURAL NETWORK; INDEPENDENT SAMPLES; OBJECT BOUNDARIES; SALIENT OBJECT DETECTION; SALIENT OBJECTS; SPATIAL COHERENCE; STATE OF THE ART;

PATTERN RECOGNITION;

EID: 84986290339     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2016.58     Document Type: Conference Paper

References (51)

1. R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk. Frequency-tuned salient region detection. In Computer vision and pattern recognition, 2009. cvpr 2009. ieee conference on, pages 1597-1604. IEEE, 2009.
2. R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk. Slic superpixels. Technical report, 2010.
3. S. Avidan and A. Shamir. Seam carving for contentaware image resizing. In ACM Transactions on graphics (TOG), volume 26, page 10. ACM, 2007.
4. S. Bi, G. Li, and Y. Yu. Person re-identification using multiple experts with random subspaces. Journal of Image and Graphics, 2 (2), 2014.
5. K.-Y. Chang, T.-L. Liu, H.-T. Chen, and S.-H. Lai. Fusing generic objectness and visual saliency for salient object detection. In Computer Vision (ICCV), 2011 IEEE International Conference on, pages 914-921. IEEE, 2011.
6. L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. Semantic image segmentation with deep convolutional nets and fully connected crfs. ArXiv preprint arXiv: 1412. 7062, 2014.
7. T. Chen, L. Lin, L. Liu, X. Luo, and X. Li. Disc: Deep image saliency computing via progressive representation learning. IEEE Transactions on Neural Networks and Learning Systems.
8. M. Cheng, N. J. Mitra, X. Huang, P. H. Torr, and S. Hu. Global contrast based salient region detection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 37 (3): 569-582, 2015.
9. A. Criminisi, T. Sharp, C. Rother, and P. Pérez. Geodesic image and video editing.
10. J. Dai, K. He, and J. Sun. Convolutional feature masking for joint object and stuff segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3992-4000, 2015.
11. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pages 248-255. IEEE, 2009.
12. W. Einhäuser and P. Koènig. Does luminance-contrast contribute to a saliency map for overt visual attention European Journal of Neuroscience, 17 (5): 1089-1097, 2003.
13. C. Farabet, C. Couprie, L. Najman, and Y. LeCun. Learning hierarchical features for scene labeling. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 35 (8): 1915-1929, 2013.
14. D. Gao and N. Vasconcelos. Bottom-up saliency is a discriminant process. In Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on, pages 1-6. IEEE, 2007.
15. R. Girshick. Fast r-cnn. In International Conference on Computer Vision (ICCV), 2015.
16. R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, pages 580-587. IEEE, 2014.
17. S. Goferman, L. Zelnik-Manor, and A. Tal. Contextaware saliency detection. TPAMI, 34 (10): 1915-1926, 2012.
18. K. He, X. Zhang, S. Ren, and J. Sun. Spatial pyramid pooling in deep convolutional networks for visual recognition. In Computer Vision-ECCV 2014, pages 346-361. Springer, 2014.
19. Y. Jia and M. Han. Category-independent object-level saliency detection. In Computer Vision (ICCV), 2013 IEEE International Conference on, pages 1761-1768. IEEE, 2013.
20. 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 Proceedings of the ACM International Conference on Multimedia, pages 675-678. ACM, 2014.
21. P. Jiang, H. Ling, J. Yu, and J. Peng. Salient region detection by ufo: Uniqueness, focusness and objectness. In Computer Vision (ICCV), 2013 IEEE International Conference on, pages 1976-1983. IEEE, 2013.
22. T. Judd, K. Ehinger, F. Durand, and A. Torralba. Learning to predict where humans look. In ICCV, 2009.
23. D. Klein, S. Frintrop, et al. Center-surround divergence of feature statistics for salient object detection. In Computer Vision (ICCV), 2011 IEEE International Conference on, pages 2214-2219. IEEE, 2011.
24. P. Krähenbühl and V. Koltun. Efficient inference in fully connected crfs with Gaussian edge potentials. ArXiv preprint arXiv: 1210. 5644, 2012.
25. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097-1105, 2012.
26. G. Li and Y. Yu. Visual saliency based on multiscale deep features. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015.
27. H. Li, R. Zhao, and X. Wang. Highly efficient forward and backward propagation of convolutional neural networks for pixelwise classification. ArXiv preprint arXiv: 1412. 4526, 2014.
28. Y. Li, X. Hou, C. Koch, J. M. Rehg, and A. L. Yuille. The secrets of salient object segmentation. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, pages 280-287. IEEE, 2014.
29. R. Liu, J. Cao, Z. Lin, and S. Shan. Adaptive partial differential equation learning for visual saliency detection. In CVPR, 2014.
30. T. Liu, Z. Yuan, J. Sun, J. Wang, N. Zheng, X. Tang, and H.-Y. Shum. Learning to detect a salient object. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 33 (2): 353-367, 2011.
31. J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. ArXiv preprint arXiv: 1411. 4038, 2014.
32. S. Lu, V. Mahadevan, and N. Vasconcelos. Learning optimal seeds for diffusion-based salient object detection. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, pages 2790-2797. IEEE, 2014.
33. Y.-F. Ma, L. Lu, H.-J. Zhang, and M. Li. A user attention model for video summarization. In Proceedings of the tenth ACM international conference on Multimedia, pages 533-542. ACM, 2002.
34. L. Mai, Y. Niu, and F. Liu. Saliency aggregation: A data-driven approach. In Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, pages 1131-1138. IEEE, 2013.
35. S. Mallat. A wavelet tour of signal processing. Academic press, 1999.
36. D. Martin, C. Fowlkes, D. Tal, and J. Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on, volume 2, pages 416-423. IEEE, 2001.
37. V. Navalpakkam and L. Itti. An integrated model of top-down and bottom-up attention for optimizing detection speed. In Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, volume 2, pages 2049-2056. IEEE, 2006.
38. D. Parkhurst, K. Law, and E. Niebur. Modeling the role of salience in the allocation of overt visual attention. Vision research, 42 (1): 107-123, 2002.
39. F. Perazzi, P. Krähenbühl, Y. Pritch, and A. Hornung. Saliency filters: Contrast based filtering for salient region detection. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 733-740. IEEE, 2012.
40. Y. Qin, H. Lu, Y. Xu, and H. Wang. Saliency detection via cellular automata. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 110-119, 2015.
41. X. Shen and Y. Wu. A unified approach to salient object detection via low rank matrix recovery. In CVPR, 2012.
42. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. ArXiv preprint arXiv: 1409. 1556, 2014.
43. K. Wang, L. Lin, J. Lu, C. Li, and K. Shi. Pisa: Pixelwise image saliency by aggregating complementary appearance contrast measures with edge-preserving coherence. Image Processing, IEEE Transactions on, 24 (10): 3019-3033, Oct 2015.
44. L. Wang, H. Lu, X. Ruan, and M.-H. Yang. Deep networks for saliency detection via local estimation and global search. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3183-3192, 2015.
45. P. Wang, G. Zeng, R. Gan, J. Wang, and H. Zha. Structure-sensitive superpixels via geodesic distance. International journal of computer vision, 103 (1): 1-21, 2013.
46. R. Wu, Y. Yu, and W. Wang. Scale: Supervised and cascaded laplacian eigenmaps for visual object recognition based on nearest neighbors. In CVPR, 2013.
47. S. Xie and Z. Tu. Holistically-nested edge detection. ArXiv preprint arXiv: 1504. 06375, 2015.
48. Z. Yan, H. Zhang, R. Piramuthu, V. Jagadeesh, D. De-Coste, W. Di, and Y. Yu. Hd-cnn: Hierarchical deep convolutional neural networks for large scale visual recognition. In Proceedings of the IEEE International Conference on Computer Vision, pages 2740-2748, 2015.
49. C. Yang, L. Zhang, H. Lu, X. Ruan, and M.-H. Yang. Saliency detection via graph-based manifold ranking. In Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, pages 3166-3173. IEEE, 2013.
50. R. Zhao, W. Ouyang, H. Li, and X. Wang. Saliency detection by multi-context deep learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1265-1274, 2015.
51. W. Zhu, S. Liang, Y. Wei, and J. Sun. Saliency optimization from robust background detection. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, pages 2814-2821. IEEE, 2014.