A unified spectral-domain approach for saliency detection and its application to automatic object segmentation

IEEE Transactions on Image Processing

Volumn 21, Issue 3, 2012, Pages 1272-1283

  Jung, Chanho ^a   Kim, Changick ^a  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

Author keywords

Automatic object segmentation; graph cut; human visual system (HVS); saliency detection; spectral domain analysis

Indexed keywords

GRAPH CUT; HUMAN VISUAL SYSTEMS; OBJECT SEGMENTATION; SALIENCY DETECTION; SPECTRAL DOMAIN ANALYSIS;

ALGORITHMS;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ATTENTION; AUTOMATED PATTERN RECOGNITION; HUMAN; METHODOLOGY; VISION;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; ATTENTION; HUMANS; PATTERN RECOGNITION, AUTOMATED; VISUAL PERCEPTION;

EID: 84863121397     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2011.2164420     Document Type: Article

References (37)

1. M. Fussenegger, A. Opelt, A. Pinz, and P. Auer, "Object recognition using segmentation for feature detection," in Proc. IEEE Int. Conf. Pattern Recognit., 2004, pp. 41-44.
2. K. Hirata, E. Kasutani, and Y. Hara, "On image segmentation for object-based image retrieval," in Proc. IEEE Int. Conf. Pattern Recognit., 2002, pp. 1031-1034. (Pubitemid 44844662)
3. W. A. Barrett and A. S. Cheney, "Object-based image editing," ACM Trans. Graph., vol. 21, no. 3, pp. 777-784, Jul. 2002.
4. E. N. Mortensen and W. A. Barrett, "Intelligent scissors for image composition," in Proc. ACM SIGGRAPH, 1995, pp. 191-198.
5. Y.-Y. Chuang, B. Curless, D. H. Salesin, and R. Szeliski, "A Bayesian approach to digital matting," in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., 2001, pp. 264-271.
6. Y. Boykov and M. P. Jolly, "Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images," in Proc. IEEE Int. Conf. Comput. Vis., 2001, pp. 105-112. (Pubitemid 32794944)
7. Y. Boykov and G. Funka-Lea, "Graph cuts and efficient N-D image segmentation," Int. J. Comput. Vis., vol. 70, no. 2, pp. 109-131, Nov. 2006. (Pubitemid 44127252)
8. C. Rother, V. Kolmogorov, and A. Blake, ""GrabCut"- Interactive foreground extraction using iterated graph cuts," in Proc. ACM SIGGRAPH, 2004, pp. 309-314.
9. Y. Li, J. Sun, C.-K. Tang, and H.-Y. Shum, "Lazy snapping," in Proc. ACM SIGGRAPH, 2004, pp. 303-308.
10. P. Kohli and P. H. S. Torr, "Dynamic graph cuts for efficient inference in Markov random fields," IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 12, pp. 2079-2088, Dec. 2007. (Pubitemid 350124058)
11. A. Oliva, A. Torralba, M. S. Castelhano, and J. M. Henderson, "Topdown control of visual attention in object detection," in Proc. IEEE Int. Conf. Image Process., 2003, pp. 253-256.
12. D. Gao and N. Vasconcelos, "Discriminant saliency for visual recognition from cluttered scenes," in Proc. Neural Inf. Process. Syst., 2004, pp. 481-488.
13. 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. (Pubitemid 128742001)
14. S.-J. Park, J.-K. Shin, and M. Lee, "Biologically inspired saliency map model for bottom-up visual attention," in Proc. British Mach. Vis. Conf., 2002, pp. 418-426.
15. D. Gao and N. Vasconcelos, "Bottom-up saliency is a discriminant process," in Proc. IEEE Int. Conf. Comput. Vis., 2007, pp. 1-6.
16. H. J. Seo and P. Milanfar, "Nonparametric bottom-up saliency detection by self-resemblance," in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit. Workshops, 2009, pp. 45-52.
17. D.Walther and C. Koch, "Modeling attention to salient proto-objects," Neural Netw., vol. 19, no. 9, pp. 1395-1407, Nov. 2006. (Pubitemid 44708603)
18. L. Zhang, M. Tong, T. Marks, H. Shan, and G. Cottrell, "SUN: A Bayesian framework for saliency using natural statistics," J. Vis., vol. 8, no. 7, pp. 1-20, Dec. 2008.
19. Y.-F. Ma and H.-J. Zhang, "Contrast-based image attention analysis by using fuzzy growing," in Proc. ACM Int. Conf. Multimedia, 2003, pp. 374-381.
20. D. Gao, V. Mahadevan, and N. Vasconcelos, "On the plausibility of the discriminant center-surround hypothesis for visual saliency," J. Vis., vol. 8, no. 7, pp. 1-18, Jun. 2008.
21. T. Liu, J. Sun, N.-N. Zheng, X. Tang, and H.-Y. Shum, "Learning to detect a salient object," in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., 2007, pp. 1-8.
22. S. Goferman, L. Zelnik-Manor, and A. Tal, "Context-aware saliency detection," in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., 2010, pp. 2376-2383.
23. X. Hou and L. Zhang, "Saliency detection: A spectral residual approach," in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., 2007, pp. 1-8.
24. C. Jung, B. Kim, and C. Kim, "Automatic segmentation of salient objects using iterative reversible graph cut," in Proc. IEEE Int. Conf. Multimedia Expo, 2010, pp. 590-595.
25. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity," IEEE Trans. Image Process., vol. 13, no. 4, pp. 600-612, Apr. 2004.
26. W. S. Geisler and M. S. Banks, "Visual performance," in Handbook of Optics, M. Bass, Ed. New York: McGraw-Hill, 1995.
27. Z. Wang and A. C. Bovik, "Embedded foveation image coding," IEEE Trans. Image Process., vol. 10, no. 10, pp. 1397-1410, Oct. 2001. (Pubitemid 32981815)
28. J. Shi and C. Tomasi, "Good features to track," in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit. Workshops, 1994, pp. 593-600.
29. T. Joachims, SVM-Light [Online]. Available: http://www.cs.cornell.edu/ People/tj/
30. Y. Fu, J. Cheng, Z. Li, and H. Lu, "Saliency cuts: An automatic approach to object segmentation," in Proc. IEEE Int. Conf. Pattern Recognit., 2008, pp. 1-4.
31. K. Fukuchi, K. Miyazato, A. Kimura, S. Takagi, and J. Yamato, "Saliency-based video segmentation with graph cuts and sequentially updated priors," in Proc. IEEE Int. Conf. Multimedia Expo, 2009, pp. 638-641.
32. J. Han, K. N. Ngan, M. Li, and H.-J. Zhang, "Unsupervised extraction of visual attention objects in color images," IEEE Trans. Circuits Syst. Vid. Technol., vol. 16, no. 1, pp. 141-145, Jan. 2006. (Pubitemid 43172623)
33. M. Everingham, L. Van Gool, C.Williams, J.Winn, and A. Zisserman, "The PASCAL visual object classes (VOC) challenge," Int. J. Comput. Vis., vol. 88, no. 2, pp. 303-338, Jun. 2010.
34. 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, Nov. 2009.
35. Y. Boykov and V. Kolmogorov, "An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision," IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 9, pp. 1124-1137, Sep. 2004.
36. H. Yu, J. Li, Y. Tian, and T. Huang, "Automatic interesting object extraction from images using complementary saliency maps," in Proc. ACM Int. Conf. Multimedia, 2010, pp. 891-894.
37. R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk, "Frequencytuned salient region detection," in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., 2009, pp. 1597-1604.