Layered graph matching by composite cluster sampling with collaborative and competitive interactions

2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009

Volumn , Issue , 2009, Pages 1351-1358

  Lin, Liang ^a,b,c   Zeng, Kun ^b   Liu, Xiaobai ^b   Zhu, Song Chun ^b,c  

^a BEIJING INSTITUTE OF TECHNOLOGY   (China)

^b Lotus Hill Research Institute   (China)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COLOR; COMPUTER VISION; GRAPH ALGORITHMS; INFERENCE ENGINES; MARKOV CHAINS; OBJECT DETECTION; PATTERN MATCHING; STOCHASTIC SYSTEMS;

CLUTTERED BACKGROUNDS; COMPETITIVE INTERACTIONS; COMPOSITE CLUSTERS; CONNECTED COMPONENT; INFERENCE ALGORITHM; MARKOV CHAIN MONTE CARLO; STATE-OF-THE-ART APPROACH; STOCHASTIC SAMPLING;

CLUSTERING ALGORITHMS;

EID: 70450195571     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2009.5206585     Document Type: Conference Paper

References (25)

1. A. Barbu and S.C. Zhu, Generalizing Swendsen-Wang for Image Analysis, Journal of Computational and Graphical Statistics, 16(4): 877-900, 2007.
2. S. Belongie, et al., Shape Matching and Object Recognition Using Shape Contexts. TPAMI, 24(4):509-522, 2002.
3. A.C. Berg, et al., Shape Matching and Object Recognition using Low Distortion Correspondences, CVPR, 2005.
4. G. Borgefors, Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm, TPAMI, 10(6): 849-865, 1988.
5. H. Chui and A. Rangarajan, A new point matching algorithm for non-rigid registration, Computer Vision and Image Understanding, 89(2):114-141, 2003.
6. I. Dryden and K. Mardia, Statistical Shape Analysis, JohnWiley and Sons, 1998.
7. P.F. Felzenszwalb, et al., Hierarchical Matching of Deformable Shapes. CVPR, 2007.
8. K. Grauman and T. Darrell, Pyramid match kernels: Discriminative calssification with sets of image features, Technical Report MIT-CSAIL-TR-2006-020, MIT, 2006.
9. C. E. Guo, S. C. Zhu, and Y. N.Wu, "Primal Sketch: Integrating Texture and Structure", Computer Vision and Image Understanding, 106(1): 5-19, 2007.
10. D. G. Lowe, Distinctive Image Features from Scale-Invariant Keypoints, IJCV, 60(2): 91-110, 2004.
11. L. Latecki, et al., Shape descriptors for non-rigid shapes with a single closed contour. CVPR, 2000.
12. L. Lin, S.C. Zhu, and Y. Wang, Layered Graph Match with Graph Editing, CVPR, 2007.
13. H. Ling and D. W. Jacobs, Using the inner-distance for classification of articulated shapes, CVPR, 2005.
14. X. Liu, L. Lin, et al., Layered Shape Matching and Registration: Stochastic Sampling with Hierarchical Graph Representation, ICPR, 2008.
15. N. Metropolis, et al., Equation of State Calculations by Fast Computing Machines. Journal of Chemical Physics, 21(6): 85- 111, 1953.
16. D. Nister, Preemptive RANSAC for live structure and motion estimation, ICCV, vol.1: 199-206, 2003.
17. G.C. Sharp, et al., ICP Registration Using Invariant Features. TPAMI, 24(1):90-102, 2002.
18. T. Sebastian, P. Klein, and B. Kimia, On aligning curves, IEEE Trans. on PAMI, 25(1):116C124, 2003.
19. K. Siddiqi, et al., Shock Graphs and Shape Matching. IJCV, 35(1): 13-32, 1999.
20. D.G. Sim, et al., Object Matching Algorithms Using Robust Hausdorff Distance Measures. TIP, 8(3): 425-429, 1999.
21. M.F. Tappen, el tal, Learning Gaussian Conditional Random Fields for Low-Level Vision, CVPR, 2007.
22. Z. Tu, et al., Shape Matching and Registration by Datadriven EM, CVIU, 103(3): 290-304, 2007.
23. X. Yang, X. Bai, L. Latecki, and Z. Tu, Improving Shape Retrieval by Learning Graph Transduction, ECCV, 2008.
24. B. Yao, et al., Introduction to A Large Scale General Purpose Groundtruth Dataset: Methodology, Annotation Tool, and Benchmarks. EMMCVPR, Springer LNCS, 2007.
25. S.C. Zhu and A. Yuille, Forms: A Flexible Object Recognition and Modeling System, IJCV, 1996.