Task-specific image partitioning

IEEE Transactions on Image Processing

Volumn 22, Issue 2, 2013, Pages 488-500

  Kim, Sungwoong ^a   Nowozin, Sebastian ^b   Kohli, Pushmeet ^b   Yoo, Chang D ^c  

^a Qualcomm Research Korea   (South Korea)

^b MICROSOFT RESEARCH   (United Kingdom)

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

Author keywords

Correlation clustering; image partitioning; linear programming relaxation; structured support vector machine

Indexed keywords

BENCHMARK DATASETS; CORRELATION CLUSTERING; DISCRIMINABILITY; DISCRIMINANT FUNCTIONS; GENERALIZATION ABILITY; HIGH-LEVEL COMPUTER VISION; IMAGE PARTITIONING; IMAGE REPRESENTATIONS; IMPROVING PERFORMANCE; LINEAR DISCRIMINANT FUNCTIONS; LINEAR PROGRAMMING RELAXATION; PARTITIONING ALGORITHMS; PRE-PROCESSING STEP; REGION-BASED; STATE-OF-THE-ART ALGORITHMS; STRUCTURED SUPPORTS; SUPERPIXELS; TASK PERFORMANCE; TASK-AWARE; TRAINING DATA;

ALGORITHMS; COMPUTER VISION; DATA PROCESSING;

SUPPORT VECTOR MACHINES;

EID: 84872306546     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2012.2218822     Document Type: Article

References (43)

1. L. Ladick̀y, C. Russell, P. Kohli, and P. H. S. Torr, "Associative hierarchical CRFs for object class image segmentation," in Proc. IEEE Int. Conf. Comput. Vis., Sep.-Oct. 2009, pp. 739-746.
2. S. Gould, R. Fulton, and D. Koller, "Decomposing a scene into geometric and semantically consistent regions," in Proc. IEEE Int. Conf. Comput. Vis., Sep.-Oct. 2009, pp. 1-8.
3. M. P. Kumar and D. Koller, "Efficiently selecting regions for scene understanding," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Dec. 2010, pp. 1-8.
4. D. Hoiem, A. A. Efros, and M. Hebert, "Recovering surface layout from an image," Int. J. Comput. Vis., vol. 75, no. 1, pp. 151-172, 2007. (Pubitemid 47120250)
5. B. Liu, S. Gould, and D. Koller, "Single image depth estimation from predicted semantic labels," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2010, pp. 1253-1260.
6. M. Everingham, L. V. Gool, C. K. I. Williams, J. Winn, and A. Zisserman. (2009). The PASCAL Visual Object Classes Challenge Results [Online]. Available: http://www.pascalnetwork. org/challenges/VOC/voc2009/workshop/index.html
7. B. Fulkerson, A. Vedaldi, and S. Soatto, "Class segmentation and object localization with superpixel neighborhoods," in Proc. IEEE Int. Conf. Comput. Vis., Sep.-Oct. 2009, pp. 670-677.
8. P. Kohli, L. Ladick̀y, and P. H. S. Torr, "Robust higher order potentials for enforcing label consistency," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2008, pp. 1-8.
9. D. Comaniciu and P. Meer, "Mean shift: A robust approach toward feature space analysis," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 5, pp. 603-619, May 2002. (Pubitemid 34550429)
10. J. Shi and J. Malik, "Normalized cuts and image segmentation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 8, pp. 888-905, Aug. 2000.
11. P. Felzenszwalb and D. Huttenlocher, "Efficient graph-based image segmentation," Int. J. Comput. Vis., vol. 59, no. 2, pp. 167-181, 2004.
12. A. Vedaldi and S. Soatt, "Quick shift and kernel methods for mode seeking," in Proc. Eur. Conf. Comput. Vis., 2008, pp. 1-14.
13. P. Arbeláez, M. Maire, C. Fowlkes, and J. Malik, "Contour detection and hierarchical image segmentation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 5, pp. 898-916, May 2011.
14. T. Cour, F. Benezit, and J. Shi, "Spectral segmentation with multiscale graph decomposition," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2005, pp. 1124-1131.
15. N. Bansal, A. Blum, and S. Chawla, "Correlation clustering," Mach. Learn., vol. 56, pp. 89-113, Dec. 2004.
16. I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun, "Large margin methods for structured and interdependent output variables," J. Mach. Learn. Res., vol. 6, pp. 1453-1484, Dec. 2005.
17. C. Fowlkes, D. Martin, and J. Malik, "Learning affinity functions for image segmentation: Combining patch-based and gradient-based approaches," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2003, pp. 54-61.
18. F. Bach and M. I. Jordan, "Learning spectral clustering," in Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2003.
19. N. Shental, A. Zomet, T. Hertz, and Y. Welss, "Pairwise clustering and graphical models," in Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2004.
20. T. Cour, N. Gogin, and J. Shi, "Learning spectral graph segmentation," in Proc. Int. Conf. Artif. Intell. Stat., 2005, pp. 1-8.
21. S. Turaga, K. Briggman, M. Helmstaedter, W. Denk, and H. Seung, "Maximin affinity learning of image segmentation," in Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2009.
22. T. Finley and T. Joachims, "Supervised clustering with support vector machines," in Proc. Int. Conf. Mach. Learn., 2005, pp. 217-224. (Pubitemid 43183336)
23. B. Taskar, "Learning structured prediction models: A large margin approach," Ph.D. thesis, Dept. Comput. Sci., Stanford Univ., Stanford, CA, 2004.
24. T. Finley and T. Joachims, "Training structural SVMs when exact inference is intractable," in Proc. Int. Conf. Mach. Learn., 2008, pp. 304-311.
25. A. Kulesza and F. Pereira, "Structured learning with approximate inference," in Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2007.
26. A. F. T. Martins, N. A. Smith, and E. P. Xing, "Polyhedral outer approximations with application to natural language parsing," in Proc. Int. Conf. Mach. Learn., 2009, pp. 1-8.
27. S. Chopra and M. R. Rao, "The partition problem," Math. Program., vol. 59, nos. 1-3, pp. 87-115, 1993.
28. S. Nowozin and S. Jegelka, "Solution stability in linear programming relaxations: Graph partitioning and unsupervised learning," in Proc. Int. Conf. Mach. Learn., 2009, pp. 1-8.
29. O. Pele and M. Werman, "Fast and robust earth mover's distances," in Proc. IEEE Int. Conf. Comput. Vis., 2009, pp. 1-8.
30. T. Leung and J. Malik, "Representing and recognizing the visual appearance of materials using three-dimensional textons," Int. J. Comput. Vis., vol. 43, no. 1, pp. 29-44, 2001. (Pubitemid 32720091)
31. D. Batra, R. Sukthankar, and T. Chen, "Learning class-specific affinities for image labelling," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2008, pp. 1-8.
32. T. Cour, B. Sapp, A. Nagle, and B. Taskar, "Talking pictures: Temporal grouping and dialog-supervised person recognition," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Dec. 2010, pp. 1-8.
33. W. M. Rand, "Objective criteria for the evaluation of clustering methods," J. Amer. Stat. Assoc., vol. 66, no. 336, pp. 846-850, 1971.
34. M. Meila, "Computing clusterings: An axiomatic view," in Proc. Int. Conf. Mach. Learn., 2005, pp. 577-584.
35. J. Freixenet, X. Munoz, D. Raba, J. Marti, and X. Cufi, "Yet another survey on image segmentation: Region and boundary information integration," in Proc. Eur. Conf. Comput. Vis., 2002, pp. 408-422.
36. O. Veksler, Y. Boykov, and P. Mehrani, "Superpixels and supervoxels in an energy optimization framework," in Proc. Eur. Conf. Comput. Vis., 2010, pp. 211-224.
37. C.-C. Chang and C.-J. Lin. (2001). LIBSVM: A Library for Support Vector Machines [Online]. Available: http://www.csie.ntu.edu.tw/∼cjlin/libsvm
38. A. Vedaldi and B. Fulkerson. (2008). VLFeat: An Open and Portable Library of Computer Vision Algorithms [Online]. Available: http://www.vlfeat.org/
39. 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 Proc. 8th Int. Conf. Comput. Vis., vol. 2. Jul. 2001, pp. 416-423. (Pubitemid 32795086)
40. T. Kim, K. Lee, and S. Lee, "Learning full pairwise affinities for spectral segmentation," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2010, pp. 2101-2108.
41. S. R. Rao, H. Mobahi, A. Y. Yang, S. S. Sastry, and Y. Ma, "Natural image segmentation with adaptive texture and boundary encoding," in Proc. Asian Conf. Comput. Vis., 2009, pp. 1-11.
42. J. Shotton, J. Winn, C. Rother, and A. Criminisi, "TextonBoost: Joint apprearence, shape and context modeling for multi-class object recognition and segmentation," in Proc. Eur. Conf. Comput. Vis., 2006, pp. 1-15. (Pubitemid 44029764)
43. T. Malisiewicz and A. A. Efros, "Improving spatial support for objects via multiple segmentations," in Proc. British Mach. Vis. Conf., 2007, pp. 1-10.