Cong Yang
ABSTRACT
This paper proposes a novel object matching algorithm based on shape contours. In order to ensure low computational complexity in shape representation, our descriptor is composed by a small number of interest points which are generated by considering both curvatures and the overall shape trend. To effectively describe each point of interest, we introduce a simple and highly discriminative point descriptor, namely Point Context, which represents its geometrical and topological location. For shape matching, we observed that the correspondences are not only dependent on the similarities between these single points in different objects, but they are also related to the geometric relations between multiple points of interest in the same object. Therefore, a high-order graph matching formulation is introduced to merge the single point similarities and the similarities between point triangles. The main contributions of this paper include (i) the introduction of a novel shape descriptor with robust shape points and their descriptors and (ii) the implementation of a high-order graph matching algorithm that solves the shape matching problem. Our method is validated through a series of object retrieval experiments on four datasets demonstrating its robustness and accuracy.
- X. Bai and L. Latecki. Path similarity skeleton graph matching. PAMI, 30(7):1282--1292, 2008. Google Scholar
Digital Library
- X. Bai, L. Latecki, and W. yu Liu. Skeleton pruning by contour partitioning with discrete curve evolution. PAMI, 29(3):449--462, 2007. Google ScholarDigital Library
- S. Belongie, J. Malik, and J. Puzicha. Shape matching and object recognition using shape contexts. PAMI, 24(4):509--522, 2002. Google ScholarDigital Library
- O. Duchenne, F. Bach, I.-S. Kweon, and J. Ponce. A tensor-based algorithm for high-order graph matching. PAMI, 33(12):2383--2395, Dec 2011. Google ScholarDigital Library
- C. Feinen, C. Yang, O. Tiebe, and M. Grzegorzek. Shape matching using point context and contour segments. In ACCV. Springer LNCS, November 2014.Google Scholar
- L. Latecki, Q. Wang, S. Koknar-Tezel, and V. Megalooikonomou. Optimal subsequence bijection. In ICMD, pages 565--570, Oct 2007. Google ScholarDigital Library
- L. J. Latecki and R. Lakämper. Convexity rule for shape decomposition based on discrete contour evolution. CVIU, 73:441--454, 1999. Google ScholarDigital Library
- C. Lu, L. Latecki, N. Adluru, X. Yang, and H. Ling. Shape guided contour grouping with particle filters. In ICCV, pages 2288--2295, September 2009.Google Scholar
- T. Ma and L. Latecki. From partial shape matching through local deformation to robust global shape similarity for object detection. In CVPR, pages 1441--1448, June 2011. Google ScholarDigital Library
- Y. Rubner, C. Tomasi, and L. J. Guibas. The earth mover's distance as a metric for image retrieval. Int. J. Comput. Vision, 40(2):99--121, November 2000. Google ScholarDigital Library
- S. P. Tadeusiewicz R., Ogiela M. R. Notes on a linguistic description as the basis for automatic image understanding. Int. Journal of Applied Mathematics and Computer Science, 19(1):143--150, 2009. Google ScholarDigital Library
- L. Torresani, V. Kolmogorov, and C. Rother. Feature correspondence via graph matching: Models and global optimization. In ECCV, volume 5303, pages 596--609. Springer Berlin Heidelberg, 2008. Google ScholarDigital Library
- O. van Kaick, H. Zhang, G. Hamarneh, and D. Cohen-Or. A survey on shape correspondence. Computer Graphics Forum, 30(6):1681--1707, 2011.Google ScholarCross Ref
- Y. Zeng, C. Wang, Y. Wang, X. Gu, D. Samaras, and N. Paragios. Dense non-rigid surface registration using high-order graph matching. In CVPR, pages 382--389, June 2010.Google ScholarCross Ref
Index Terms
- Shape-based Object Matching Using Point Context
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