Multiclass Labeling of Very High-Resolution Remote Sensing Imagery by Enforcing Nonlocal Shared Constraints in Multilevel Conditional Random Fields Model

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volumn 9, Issue 7, 2016, Pages 2854-2867

  Zhang, Tong ^a,b   Yan, Wenjie ^a,b   Li, Jing ^c   Chen, Jie ^d  

^a WUHAN UNIVERSITY   (China)

^b The Collaborative Innovation Center for Geospatial Technology   (China)

^c UNIVERSITY OF DENVER   (United States)

^d CENTRAL SOUTH UNIVERSITY   (China)

Author keywords

Conditional random fields (CRF); cosegmentation; higher order potentials; multiclass labeling; nonlocal constraints

Indexed keywords

IMAGE SEGMENTATION; REMOTE SENSING; SEMANTICS;

CO SEGMENTATIONS; CONDITIONAL RANDOM FIELD; HIGHER ORDER POTENTIALS; MULTI-CLASS LABELING; NONLOCAL CONSTRAINTS;

RANDOM PROCESSES;

IMAGE CLASSIFICATION; IMAGE RESOLUTION; PIXEL; REMOTE SENSING; SEGMENTATION; STATISTICAL ANALYSIS;

EID: 84954045719     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2015.2510367     Document Type: Article

References (54)

1. K. Chen, P. Jian, Z. Zhou, J. Guo, and D. Zhang, "Semantic annotation of high-resolution remote sensing images via Gaussian Process multiinstance multilabel learning," IEEE Geosci. Remote Sens. Letters, vol. 10, no. 6, pp. 1285-1289, Nov. 2013.
2. X. Zhang, P. Xiao, X. Feng, J.Wang, and Z.Wang, "Hybrid region merging method for segmentation of high-resolution remote sensing images," ISPRS J. Photogramm. Remote Sens., vol. 98, pp. 19-28, Dec. 2014.
3. L. Shen, H. Tang, Y. Chen, A. Gong, J. Li, and W. Yi, "A semisupervised latent Dirichlet allocation model for object-based classification of VHR panchromatic satellite image." IEEE Geosci. Remote Sens. Lett., vol. 11, no. 4, pp. 863-867, Apr. 2014.
4. T. Mei, L. An, and Q. Li, "Supervised segmentation of remote sensing image using reference descriptor," IEEE Geosci. Remote Sens. Lett., vol. 12, no. 5, pp. 938-942, May 2015.
5. L. Ladický, P. Sturgess, K. Alahari, C. Russell, and P. Torr, "What, where and how many? combining object detectors and CRFs," in Proc. Eur. Conf. Comput. Vis. (ECCV'10), 2010, pp. 424-437.
6. G. Floros, K. Rematas, and B. Leibe, "Multi-class image labeling with top-down segmentation and generalized robust PN potentials," in Proc. Brit. Mach. Vis. Conf. (BMVC'11), 2011, pp. 79.1-79.11.
7. L. Ladický, C. Russell, P. Kohli, and P. Torr, "Associative hierarchical random fields," IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 6, pp. 1056-1077, Jun. 2014.
8. J. Wegner, R. Hänsch, A. Thiele, and U. Soergel, "Building detection from one orthophoto and high-Resolution InSAR data using conditional random fields," IEEE J. Sel. Topics Appl. Earth Observ., vol 4, no. 1, pp. 83-91, Mar. 2011.
9. P. Zhong and R. Wang, "Modeling and classifying hyperspectral imagery by CRFs with sparse higher order potentials," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 2, pp. 688-705, Feb. 2011.
10. S. Paisitkriangkrai, J. Sherrah, P. Janney, and A. Van-Den Hengel, "Effective semantic pixel labeling with convolutional networks and conditional random fields," in Proc. Comput. Vis. Pattern Recog. Workshops (CVPRW'15), 2015, pp. 36-43.
11. M. Volph and V. Ferrari, "Semantic segmentation of urban scenes by learning local class interactions," in Proc. Comput. Vis. Pattern Recog. Workshops (CVPRW'15), 2015, pp. 1-9.
12. X. Lan, S. Roth, D. Huttenlocher, and M. Black, "Efficient belief propagation with learned higher-order markov random fields," in Proc. Eur. Conf. Comput. Vis. (ECCV'06), 2006, pp. 268-282.
13. N. Komodakis and N. Paragios, "Beyong pairwise energies: efficient optimization for higher-order MRFs," in Proc. Comput. Vis. Pattern Recog. (CVPR'09), 2009, pp. 2985-2992.
14. K. Park and S. Gould, "On learning higher-order consistency potentials for multi-class pixel labeling," in Proc. Eur. Conf. Comput. Vis. (ECCV'12), 2012, pp. 202-215.
15. X. Lan, S. Roth, D. Huttenlocher, and M. Black, "Efficient belief propagation with learned higher-order markov random fields," in Proc. Eur. Conf. Comput. Vis. (ECCV'06), 2006, pp, 269-282.
16. P. Kohli, M. Kumar, and P. Torr, "P3 & beyond: Solving energies with higher order cliques," in Proc. Comput. Vis. Pattern Recog. (CVPR'07), 2007, pp. 1-8.
17. P. Kohli, L. Ladický, and P. Torr, "Robust higher order potentials for enforcing label consistency," Int. J. Comput. Vision., vol. 82, pp. 302-324, May 2009.
18. S. Gould, "Multiclass pixel labeling with non-local matching constraints," in Proc. Comput. Vis. Pattern Recog. (CVPR'12), 2012, pp. 2783-2790.
19. S. Gould, "DARWIN: A framework for machine learning and computer vision research and development," J. Mach. Learn. Res. vol. 13, pp. 3533-3537, Dec. 2012.
20. X. Boix, J. Gonfaus, J. van de Weijer, A. Bagdanov, J. Serrat, and J. Gonzàlez, "Harmony potentials: fusing global and local scale for semantic image segmentation," Int. J. Comput. Vision., vol. 96, no. 1, pp. 83-102, Jan. 2012.
21. X. He, R. Zemel, and M. Carreira-Perpinan, "Multiscale conditional random fields for image labeling," in Proc. Comput. Vis. Pattern Recog. (CVPR'04), 2004, pp. 695-702.
22. S. Kumar and M. Hebert, "A hierarchical field framework for unified context-based classification," in Proc. IEEE Conf. Comput. Vis. (ICCV'05), 2005, vol. 2, pp. 1284-1291.
23. T. Toyoda and O. Hasegawa, "Random fields model for integrating of local information and global information," IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 8, pp. 1484-1489, Aug. 2008.
24. Q. Huang, M. Han, B. Hu, and S. Ioffe, "A hierarchical conditional random field model for labeling and segmenting images for street scenes," in Proc. Comput. Vis. Pattern Recog. (CVPR'11), 2011, pp. 1953-1960.
25. V. Kolmogorov and R. Zabih, "What energy functions can be minimized via graph cuts?" IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 2, pp. 147-159, Feb. 2004.
26. C. Rother, V. Kolmogorov, T. Minka, and A. Blake, "Cosegmentation of image pairs by histogram matching-Incorporating a global constraints into MRFs," in Proc. Comput. Vis. Pattern Recog. (CVPR'06), 2006, pp. 993-1000.
27. D. Hochbaum and V. Singh, "An efficient algorithm for cosegmentation," in Proc. IEEE Conf. Comput. Vis. (ICCV'09), 2009, pp. 269-276.
28. A. Joulin, F. Bach, and J. Ponce, "Discriminative clustering for image cosegmentation," in Proc. Comput. Vis. Pattern Recog. (CVPR'10), 2010, pp. 1943-1950.
29. S. Vicente, V. Kolmogorov, and C. Rother, "Cosegmentation revisited: Models and optimization," in Proc. Eur. Conf. Comput. Vis. (ECCV'10), 2010, pp. 465-479.
30. Y. Chai, V. Lempitsky, and Z. Zisserman, "BiCoS: A bi-level cosegmentation method for image classification," in Proc. Eur. Conf. Comput. Vis. (ECCV'11), 2011, pp. 2579-2586.
31. L. Mukherjee, V. Singh, and J. Peng, "Scale invariant cosegmentation for image groups," in Proc. Comput. Vis. Pattern Recog. (CVPR'11), 2011, pp. 1881-1888.
32. A. Joulin, F. Bach, and J. Ponce, "Multi-class cosegmentation," in Proc. Comput. Vis. Pattern Recog. (CVPR'12), 2012, pp. 542-549.
33. G. Kim, E. Xing, L. Fei-Fei, and T. Kanade, "Distributed cosegmentation via submodular optimization on anisotropic diffusion," in Proc. IEEE Conf. Comput. Vis. (ICCV'11), 2011, pp. 169-176.
34. G. Kim and E. Xing, "On multiple foreground cosegmentation," in Proc. Comput. Vis. Pattern Recog. (CVPR'12), 2012, pp. 837-844.
35. H. Li, F. Meng, Q. Wu, and B. Luo, "Unsupervised multiclass region cosegmentation via ensemble clustering and energy minimization," IEEE Trans. Circuits Syst. Video Technol., vol. 25, no. 5, pp 789-801, May 2014.
36. Y. Li, Z. Zhou, andW.Wu, "A hierarchical graph model for object cosegmentation," EURASIP J. Image Video Process, vol. 2013, p. 11, Dec. 2013.
37. K. Cai, W. Shao, X. Yin, and G. Liu, "Co-segmentation of aircrafts from high-resolution satellite images," in Proc. Signal Process. (ICSP), 2012, vol. 2, pp. 993-996.
38. 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.
39. D. Blei, A. Ng, and M. Jordan, "Latent Dirichlet allocation," J. Mach. Learn. Res. vol, 3, pp. 993-1022, Mar. 2003.
40. B. Russell, A. Efros, J. Sivic, W. Freeman, and A. Zisserman, "Using multiple segmentations to discover objects and their extents in image collections," in Proc. Comput. Vis. Pattern Recog. (CVPR'06), 2006, pp. 1605-1614.
41. X. He, R. Zemel, and M. Carreira-Perpinan, "Multiscale conditional random fields for image labeling," in Proc. Comput. Vis. Pattern Recog. (CVPR'04), 2004, pp. 695-702.
42. S. Kumar and M. Hebert, "A hierarchical field framework for unified context-based classification," in Proc. IEEE Conf. Comput. Vis. (ICCV'05), 2005, pp. 1284-1291.
43. J. Shotton, J. Winn, C. Rother, and A. Criminisi, "TextonBoost for image understanding: Multi-class object recognition and segmentation by Jointly modeling texture, layout, and context," Int. J. Comput. Vision., vol. 81, pp. 2-23, Jan. 2009.
44. C. Rother, V. Kolmogorov, and A. Blake, "GrabCut - Interactive foreground extraction using iterated graph cuts," ACM Trans. Graph., vol. 23, 309-314, Aug. 2004
45. Y. Boykov and M.-P. Jolly, "Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images," in Proc. IEEE Conf. Comput. Vis. (ICCV'11), 2011, pp. 105-112.
46. Y. Boykov, O. Veksler, and R. Zabih, "Fast approximate energy minimization via graph cuts," IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 11, pp. 1222-1239, Nov. 2001.
47. A. Faktor and M. Irani, "Clustering by composition - Unsupervised discovery of image categories," IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 6, pp. 1092-1106, Jun. 2014.
48. G. Kim, E. Xing, L. Fei-Fei, and T. Kanade, "Distributed cosegmentation via submodular optimization on anisotropic diffusion," in Proc. IEEE Conf. Comput. Vis. (ICCV'11), 2011, pp. 169-176.
49. J. Kittler. "Combining classifiers: A theoretical framework," Pattern Anal. Appl., vol. 1, pp. 18-27, Mar. 1998.
50. D. Lowe, "Distinctive image features from scale-invariant keypoints," Int. J. Comput. Vision., vol. 60, pp. 91-110, Nov. 2004.
51. K. van de Sande, T. Gevers, and C. Snoek,"Evaluation of color descriptors for object and scene recognition," IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 9, pp. 1582-1596, Sep. 2010.
52. T. Ojala, M. Pietikainen, and D. Harwood, "Performance evaluation of texture measures with classification based on Kullback discrimination of distributions," in Proc. Int. Conf. Pattern. Recog (ICPR'94), 1994, vol. 1, pp. 582-585.
53. M. Everingham, S. Ali Eslami, L. Van Gool, C. Willams, J. Winn, and A. Zisserman, "The PASCAL visual object classes challenge: A retrospective," Int. J. Comput. Vision., vol. 111, pp. 98-136, Jan. 2015.
54. C. Pantofaru, C. Schmid, and M. Hebert, "Object recognition by integrating multiple image segmentations," in Proc. Eur. Conf. Comput. Vis. (ECCV), 2008, pp. 481-494.