Abstract
This paper investigates whether meaningful shape categories can be identified in an unsupervised way by clustering shock-trees. We commence by computing weighted and unweighted edit distances between shock-trees extracted from the Hamilton-Jacobi skeleton of 2D binary shapes. Next we use an EM-like algorithm to locate pairwise clusters in the pattern of edit-distances. We show that when the tree edit distance is weighted using the geometry of the skeleton, then the clustering method returns meaningful shape categories.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Siddiqi, K., Bouix, S., Tannenbaum, A., Zucker, S.W.: The hamilton-jacobi skeleton. ICCV (1999) 828–834
Siddiqi, K., Shokoufandeh, A., Dickinson, S.J., Zucker, S.W.: Shock graphs and shape matching. International Journal of Computer Vision, 35 (1999) 13–32
Kimia, B.B., Tannenbaum, A.R., Zucker, S.W.: Shapes, shocks, and deforamtions i. International Journal of Computer Vision, 15 (1995) 189–224
Rizzi, S.: Genetic operators for hierarchical graph clustering. Pattern Recognition Letters, 19 (1998) 1293–1300
Segen, J.: Learning graph models of shape. In: Laird, J. (ed.): Proceedings of the Fifth International Conference on Machine Learning (1988) 29–25
Sengupta, K., Boyer, K.L.: Organizing large structural modelbases. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17 (1995)
Munger, A., Bunke, H., Jiang, X.: Combinatorial search vs. genetic algorithms: A case study based on the generalized median graph problem. Pattern Recognition Letters, 20 (1999) 1271–1279
Shapiro, L.G., Haralick, R.M.: Relational models for scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 4 (1982) 595–602
Eshera, M.A., Fu, K.S.: A graph distance measure for image analysis. IEEE Transactions on Systems, Man and Cybernetics, 14 (1984) 398–407
Kittler, J., Christmas, W.J., Petrou, M.: Structural matching in computer vision using probabilistic relaxation. IEEE PAMI, 17 (1995) 749–764
Wilson, R., Hancock, E.R.: Structural matching by discrete relaxation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19 (1997) 634–648
Huet, B., Hancock, E.: Relational histograms for shape indexing. In: IEEE International Conference of Computer Vision (1998)
Sengupta, K., Boyer, K.L.: Modelbase partitioning using property matris spectra. Computer Vision and Image Understanding, 70 (1998)
Hofmann, T., Buhmann, M.: Pairwise data clustering by deterministic annealing. IEEE Tansactions on Pattern Analysis and Machine Intelligence, 19 (1997)
Shokoufandeh, A., et al.: Indexing using a spectral encoding of topological structure. In: Conference on Computer Vision and Pattern Recognition (June 1999)
Torsello, A., Hancock, E.R.: A skeletal measure of 2d shape similarity. In: Visual Form 2001. LNCS 2059 (2001)
Sarkar, S., Boyer, K.L.: Quantitative measures of change based on feature organization: Eigenvalues and eigenvectors. Computer Vision and Image Understanding, 71 (1998) 110–136
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Luo, B., Robles-Kelly, A., Torsello, A., Wilson, R.C., Hancock, E.R. (2001). Discovering Shape Categories by Clustering Shock Trees. In: Skarbek, W. (eds) Computer Analysis of Images and Patterns. CAIP 2001. Lecture Notes in Computer Science, vol 2124. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44692-3_19
Download citation
DOI: https://doi.org/10.1007/3-540-44692-3_19
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42513-7
Online ISBN: 978-3-540-44692-7
eBook Packages: Springer Book Archive