A Unified Metric Learning-Based Framework for Co-Saliency Detection

IEEE Transactions on Circuits and Systems for Video Technology

Volumn 28, Issue 10, 2018, Pages 2473-2483

  Han, Junwei ^a   Cheng, Gong ^a   Li, Zhenpeng ^a   Zhang, Dingwen ^a  

^a NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

Author keywords

Co saliency detection; feature learning; metric learning

Indexed keywords

OBJECT DETECTION; SUPPORT VECTOR MACHINES;

CO SALIENCIES; DISCRIMINATIVE FEATURES; FEATURE LEARNING; FEATURE REPRESENTATION; METRIC LEARNING; OBJECTIVE FUNCTIONS; REGULARIZATION TERMS; RESEARCH INTERESTS;

FEATURE EXTRACTION;

EID: 85053984868     PISSN: 10518215     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCSVT.2017.2706264     Document Type: Article

References (57)

1. H. Li and K. N. Ngan, "A co-saliency model of image pairs, " IEEE Trans. Image Process., vol. 20, no. 12, pp. 3365-3375, Dec. 2011.
2. D. Zhang, J. Han, C. Li, and J. Wang, "Co-saliency detection via looking deep and wide, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2015, pp. 2994-3002.
3. H. Li, F. Meng, and K. N. Ngan, "Co-salient object detection from multiple images, " IEEE Trans. Multimedia, vol. 15, no. 8, pp. 1896-1909, Dec. 2013.
4. D. Zhang, D. Meng, C. Li, L. Jiang, Q. Zhao, and J. Han, "A selfpaced multiple-instance learning framework for co-saliency detection, " in Proc. IEEE Int. Conf. Comput. Vis., Dec. 2015, pp. 594-602.
5. H. Fu, X. Cao, and Z. Tu, "Cluster-based co-saliency detection, " IEEE Trans. Image Process., vol. 22, no. 10, pp. 3766-3778, Oct. 2013.
6. D. Zhang, J. Han, J. Han, and L. Shao, "Cosaliency detection based on intrasaliency prior transfer and deep intersaliency mining, " IEEE Trans. Neural Netw. Learn. Syst., vol. 27, no. 6, pp. 1163-1176, Jun. 2016.
7. Z. Liu, W. Zou, L. Li, L. Shen, and O. Le Meur, "Co-saliency detection based on hierarchical segmentation, " IEEE Signal Process. Lett., vol. 21, no. 1, pp. 88-92, Jan. 2014.
8. D. Zhang, J. Han, C. Li, J. Wang, and X. Li, "Detection of co-salient objects by looking deep and wide, " Int. J. Comput. Vis., vol. 120, no. 2, pp. 215-232, Nov. 2016.
9. X. Cao, Z. Tao, B. Zhang, H. Fu, and W. Feng, "Self-adaptively weighted co-saliency detection via rank constraint, " IEEE Trans. Image Process., vol. 23, no. 9, pp. 4175-4186, Sep. 2014.
10. Y. Li, K. Fu, Z. Liu, and J. Yang, "Efficient saliency-model-guided visual co-saliency detection, " IEEE Signal Process. Lett., vol. 22, no. 5, pp. 588-592, May 2014.
11. X. Yao, J. Han, D. Zhang, and F. Nie, "Revisiting co-saliency detection: A novel approach based on two-stage multi-view spectral rotation coclustering, " IEEE Trans. Image Process., vol. 26, no. 7, pp. 3196-3209, Jul. 2017.
12. H. Fu, D. Xu, B. Zhang, S. Lin, and R. K. Ward, "Object-based multiple foreground video co-segmentation via multi-state selection graph, " IEEE Trans. Image Process., vol. 24, no. 11, pp. 3415-3424, Nov. 2015.
13. W. Wang, J. Shen, and F. Porikli, "Saliency-aware geodesic video object segmentation, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2015, pp. 3395-3402.
14. W. Wang, J. Shen, X. Li, and F. Porikli, "Robust video object cosegmentation, " IEEE Trans. Image Process., vol. 24, no. 10, pp. 3137-3148, Oct. 2015.
15. W. Wang and J. Shen, "Higher-order image co-segmentation, " IEEE Trans. Multimedia, vol. 18, no. 6, pp. 1011-1021, Jun. 2016.
16. X. Dong, J. Shen, L. Shao, and M.-H. Yang, "Interactive cosegmentation using global and local energy optimization, " IEEE Trans. Image Process., vol. 24, no. 11, pp. 3966-3977, Nov. 2015.
17. W. Wang, J. Shen, R. Yang, and F. Porikli, "A unified spatiotemporal prior based on geodesic distance for video object segmentation, " IEEE Trans. Pattern Anal. Mach. Intell., to be published. doi: 10.1109/TPAMI.2017.2662005.
18. K. Tang, A. Joulin, L.-J. Li, and L. Fei-Fei, "Co-localization in realworld images, " in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recog., Jun. 2014, pp. 1464-1471.
19. A. Joulin, K. Tang, and L. Fei-Fei, "Efficient image and video colocalization with frank-wolfe algorithm, " in Proc. Eur. Conf. Comput. Vis., 2014, pp. 253-268.
20. 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.
21. C. Yang, L. Zhang, H. Lu, R. Xiang, and M.-H. Yang, "Saliency detection via graph-based manifold ranking, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2013, pp. 3166-3173.
22. 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.
23. J. Han, D. Wang, L. Shao, X. Qian, G. Cheng, and J. Han, "Image visual attention computation and application via the learning of object attributes, " Mach. Vis. Appl., vol. 25, no. 7, pp. 1671-1683, Oct. 2013.
24. 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.
25. J. Han, D. Zhang, X. Hu, L. Guo, J. Ren, and 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.
26. 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.
27. X. Shen and Y. Wu, "A unified approach to salient object detection via low rank matrix recovery, " in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., Jun. 2012, pp. 853-860.
28. J. Hu, J. Lu, and Y.-P. Tan, "Discriminative deep metric learning for face verification in the wild, " in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., Jun. 2014, pp. 1875-1882.
29. J. Hu, J. Lu, and Y.-P. Tan, "Deep transfer metric learning, " in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., Jun. 2015, pp. 325-333.
30. Z. Huang, R. Wang, S. Shan, and X. Chen, "Projection metric learning on Grassmann manifold with application to video based face recognition, " in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., Jun. 2015, pp. 140-149.
31. J. Lu, G. Wang, W. Deng, P. Moulin, and J. Zhou, "Multi-manifold deep metric learning for image set classification, " in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., Jun. 2015, pp. 1137-1145.
32. Q. Qian, R. Jin, S. Zhu, and Y. Lin, "Fine-grained visual categorization via multi-stage metric learning, " in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., Jun. 2015, pp. 3716-3724.
33. R. G. Cinbis, J. Verbeek, and C. Schmid, "Unsupervised metric learning for face identification in TV video, " in Proc. IEEE Int. Conf. Comput. Vis., Nov. 2011, pp. 1559-1566.
34. M. Bilenko, S. Basu, and R. J. Mooney, "Integrating constraints and metric learning in semi-supervised clustering, " in Proc. IEEE Int. Conf. Mach. Learn., Jul. 2004, p. 11.
35. K. Q. Weinberger and L. K. Saul, "Distance metric learning for large margin nearest neighbor classification, " J. Mach. Learn. Res., vol. 10, pp. 207-244, Feb. 2009.
36. P. Zhu, L. Zhang, W. Zuo, and D. Zhang, "From point to set: Extend the learning of distance metrics, " in Proc. IEEE Int. Conf. Comput. Vis., Dec. 2013, pp. 2664-2671.
37. L. Lin, G. Wang, W. Zuo, X. Feng, and L. Zhang, "Cross-domain visual matching via generalized similarity measure and feature learning, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 6, pp. 1089-1102, Jun. 2017.
38. K.Wang, L. Lin, W. Zuo, S. Gu, and L. Zhang, "Dictionary pair classifier driven convolutional neural networks for object detection, " in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., Jun. 2016, pp. 2138-2146.
39. F. Wang, W. Zuo, L. Zhang, D. Meng, and D. Zhang, "A kernel classification framework for metric learning, " IEEE Trans. Neural Netw. Learn. Syst., vol. 26, no. 9, pp. 1950-1962, Sep. 2015.
40. P. Luo, L. Lin, and X. Liu, "Learning compositional shape models of multiple distance metrics by information projection, " IEEE Trans. Neural Netw. Learn. Syst., vol. 27, no. 7, pp. 1417-1428, Jul. 2016.
41. Y. Yang, F. Nie, D. Xu, J. Luo, Y. Zhuang, and Y. Pan, "A multimedia retrieval framework based on semi-supervised ranking and relevance feedback, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 4, pp. 723-742, Apr. 2012.
42. G. Ding, Y. Guo, J. Zhou, and Y. Gao, "Large-scale cross-modality search via collective matrix factorization hashing, " IEEE Trans. Image Process., vol. 25, no. 11, pp. 5427-5440, Nov. 2016.
43. C. Ge, K. Fu, F. Liu, L. Bai, and J. Yang, "Co-saliency detection via inter and intra saliency propagation, " Signal Process., Image Commun., vol. 44, pp. 69-83, May 2016.
44. Y. Yang, Z. Ma, A. G. Hauptmann, and N. Sebe, "Feature selection for multimedia analysis by sharing information among multiple tasks, " IEEE Trans. Multimedia, vol. 15, no. 3, pp. 661-669, Apr. 2013.
45. Y. Guo, G. Ding, L. Liu, J. Han, and L. Shao, "Learning to hash with optimized anchor embedding for scalable retrieval, " IEEE Trans. Image Process., vol. 26, no. 3, pp. 1344-1354, Mar. 2017.
46. X. Yao, J. Han, G. Cheng, X. Qian, and L. Guo, "Semantic annotation of high-resolution satellite images via weakly supervised learning, " IEEE Trans. Geosci. Remote Sens., vol. 54, no. 6, pp. 3660-3671, Jun. 2016.
47. R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and 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.
48. J. Winn, A. Criminisi, and T. Minka, "Object categorization by learned universal visual dictionary, " in Proc. IEEE Int. Conf. Comput. Vis., Oct. 2005, pp. 1800-1807.
49. Y. Jia and M. Han, "Category-independent object-level saliency detection, " in Proc. IEEE Int. Conf. Comput. Vis., Dec. 2013, pp. 1761-1768.
50. G. Cheng, P. Zhou, and J. Han, "Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images, " IEEE Trans. Geosci. Remote Sens., vol. 54, no. 12, pp. 7405-7415, Dec. 2016.
51. P. Zhou, G. Cheng, Z. Liu, S. Bu, and X. Hu, "Weakly supervised target detection in remote sensing images based on transferred deep features and negative bootstrapping, " Multidimension. Syst. Signal Process., vol. 27, no. 4, pp. 925-944, Oct. 2016.
52. G. Cheng, J. Han, and X. Lu, "Remote sensing image scene classification: Benchmark and state of the art, " Proc. IEEE, to be published, doi: 10.1109/JPROC.2017.2675998.
53. G. Cheng and J. Han, "A survey on object detection in optical remote sensing images, " ISPRS J. Photogramm. Remote Sens., vol. 117, pp. 11-28, Jul. 2016.
54. K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman. (2014). "Return of the devil in the details: Delving deep into convolutional nets." [Online]. Available: https://arxiv.org/abs/1405.3531
55. G. Cheng, J. Han, L. Guo, Z. Liu, S. Bu, and J. Ren, "Effective and efficient midlevel visual elements-oriented land-use classification using VHR remote sensing images, " IEEE Trans. Geosci. Remote Sens., vol. 53, no. 8, pp. 4238-4249, Aug. 2015.
56. J. Han, D. Zhang, G. Cheng, L. Guo, and 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.
57. G. Cheng, J. Han, P. Zhou, and L. Guo, "Multi-class geospatial object detection and geographic image classification based on collection of part detectors, " ISPRS J. Photogramm. Remote Sens., vol. 98, pp. 119-132, Dec. 2014.