CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2008 vol.30 Issue No.07 - July

Issue No.07 - July (2008 vol.30)

pp: 1282-1292

ABSTRACT

This paper presents a novel framework to for shape recognition based on object silhouettes. The main idea is to match skeleton graphs by comparing the shortest paths between skeleton endpoints. In contrast to typical tree or graph matching methods, we completely ignore the topological graph structure. Our approach is motivated by the fact that visually similar skeleton graphs may have completely different topological structures. The proposed comparison of shortest paths between endpoints of skeleton graphs yields correct matching results in such cases. The skeletons are pruned by contour partitioning with Discrete Curve Evolution, which implies that the endpoints of skeleton branches correspond to visual parts of the objects. The experimental results demonstrate that our method is able to produce correct results in the presence of articulations, stretching, and occlusion.

INDEX TERMS

Computing Methodologies, Artificial Intelligence, Vision and Scene Understanding, Computer vision, Shape

CITATION

Xiang Bai, Longin Jan Latecki, "Path Similarity Skeleton Graph Matching",

*IEEE Transactions on Pattern Analysis & Machine Intelligence*, vol.30, no. 7, pp. 1282-1292, July 2008, doi:10.1109/TPAMI.2007.70769REFERENCES

- [1] H. Blum, “Biological Shape and Visual Science,”
J. Theoretical Biology, vol. 38, pp. 205-287, 1973.- [4] D.P. Huttenlocher, G.A. Klanderman, and W.J. Rucklidge, “Comparing Images Using the Hausdorff Distance,”
IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 9, pp. 850-863, Sept. 1993.- [8] T. Liu and D. Geiger, “Approximate Tree Matching and Shape Similarity,”
Proc. Int'l Conf. Computer Vision, pp. 456-462, 1999.- [13] B.B. Kimia, A.R. Tannenbaum, and S.W. Zucker, “Shape, Shocks and Deformations I: The Components of 2D Shape and the Reaction-Diffusion Space,”
Int'l J. Computer Vision, vol. 15, no. 3, pp. 189-224, 1995.- [14] T.B. Sebastian, P. Klein, and B.B. Kimia, “Recognition of Shapes by Editing Shock Graphs,”
Proc. Int'l Conf. Computer Vision, pp. 755-762, 2001.- [18] K. Siddiqi, A. Shokoufandeh, S. Dickinson, and S. Zucker, “Shock Graphs and Shape Matching,”
Int'l J. Computer Vision, vol. 35, no. 1, pp. 13-32, 1999.- [20] M. Hilaga, Y. Shinagawa, T. Kohmura, and T.L. Kunii, “Topology Matching for Fully Automatic Similarity Estimation of 3D Shapes,”
Proc. ACM SIGGRAPH '01, pp. 203-212, 2001.- [21] C. Aslan and S. Tari, “An Axis Based Representation for Recognition,”
Proc. Int'l Conf. Computer Vision, pp. 1339-1346, 2005.- [26] F. Demirci, A. Shokoufandeh, S. Dickinson, Y. Keselman, and L. Bretzner, “Many-to-Many Feature Matching Using Spherical Coding of Directed Graphs,”
Proc. European Conf. Computer Vision, pp. 322-335, 2004.- [29] H.I. Choi, S.W. Choi, and H.P. Moon, “Mathematical Theory of Medial Axis Transform,”
Pacific J. Math., vol. 181, no. 1, pp. 57-88, 1997.- [32] L.J. Latecki, R. Lakämper, and U. Eckhardt, “Shape Descriptors for Non-Rigid Shapes with a Single Closed Contour,”
Proc. Computer Vision and Pattern Recognition, pp. 424-429, 2000.- [33] X. Bai, L.J. Latecki, and W.-Y. Liu, “Skeleton Pruning by Contour Partitioning,”
Proc. Int'l Conf. Discrete Geometry for Computer Imagery, pp. 567-579, 2006.- [36] L.J. Latecki, V. Megalooikonomou, Q. Wang, R. Lakämper, C.A. Ratanamahatana, and E. Keogh, “Partial Elastic Matching of Time Series,”
Proc. IEEE Int'l Conf. Data Mining, pp. 701-704, 2005.- [38] C.M. Cyr and B.B. Kimia, “3D Object Recognition Using Shape Similarity-Based Aspect Graph,”
Proc. Int'l Conf. Computer Vision, pp. 254-261, 2001.- [39] H. Sakoe and S. Chiba, “Dynamic Programming Algorithm Optimization for Spoken Word Recognition,”
IEEE Trans. Acoustics, Speech, and Signal Processing, vol. 26, no. 1, pp. 43-49, 1978.- [41] G. Kollios, M. Vlachos, and D. Gunopoulos, “Discovering Similar Multidimensional Trajectories,”
Proc. Int'l Conf. Data Eng., pp. 673-684, 2002.- [42] S. Chu, E. Keogh, D. Hart, and M. Pazzani, “Iterative Deepening Dynamic Time Warping for Time Series,”
Proc. SIAM Int'l Conf. Data Mining, 2002.- [43] B. Yi, K. Jagadish, and C. Faloutsos, “Efficient Retrieval of Similar Time Sequences under Time Warping,”
Proc. Int'l Conf. Data Eng., pp. 23-27, 1998.- [44] G. Das, D. Gunopoulos, and H. Mannila, “Finding Similar Timie Series,”
Proc. European Symp. Principles of Data Mining and Knowledge Discovery, pp. 88-100, 1997.- [45] M. Vlachos, M. Hadjieleftheriou, D. Gunopoulos, and E. Keogh, “Indexing Multi-Dimensional Time-Series with Support for Multiple Distance Measures,”
Proc. ACM SIGKDD, pp. 216-225, 2003.- [48] Z. Tu and A. Yuille, “Shape Matching and Recognition: Using Generative Models and Informative Features,”
Proc. European Conf. Computer Vision, vol. 3, pp. 195-209, 2004.- [50] X. Bai, X. Yang, D. Yu, and L.J. Latecki, “Skeleton-Based Shape Classification Using Path Similarity,”
Int'l J. Pattern Recognition and Artificial Intelligence, to appear. |