Abstract
A new method for unsupervised clustering of shapes is here proposed. This method is based on two steps: in the first step a preliminary clusterization is obtained by considering the distance among shapes after alignment with procrustes analysis [1],[2]. This step is based on the minimization of the functional θ(N cluster )=αN cluster +(1/N cluster )dist(c i ) where N cluster is the total number of clusters, dist(c i ) is the intra-cluster variability and α is an appropriate constant. In the second step, the curvature of shapes belonging to clusters obtained in the first step is examined to i) identify possible outliers and to ii) introduce a further refinement of clusters. The proposed method was tested on the Kimia, Surrey and MPEG7 shape databases and was able to obtain correct clusters, corresponding to perceptually homogeneous object categories. The proposed method was able to distinguish shapes with subtle differences, such as birds with one or two feet and to distinguish among very similar animal species....
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References
Bookstein, F.L.: Landmark methods for forms without landmarks: Localizing group differences in outline shape. Medical Image Analysis 1, 225–244 (1997)
Goodall, C.: Procrustes methods in the statistical analysis of shape. J. Royal Statistical Society, Series B 53, 285–339 (1991)
Ullman, S. (ed.): High-level Vision. MIT Press, Cambridge (1996)
Edelman, S. (ed.): Representation and Recognition in Vision. MIT Press, Cambridge (1999)
Biederman, I., Ju, G.: Surface versus edge-based determinants of visual recognitions. Cognitive Psychology 20, 38–64 (1988)
Coggins, J.: A statistical approach to multiscale, medial vision. Technical report, Department of Computer Science, University of North Carolina at Chapel Hill (1992)
Blum, H.: A transformation for extracting new descriptors of shape. In: Wathen-Dunn, W. (ed.) Models for the Perception of Speech and Visual Form, pp. 362–380. MIT Press, Cambridge, USA (1967)
Sebastian, T., Klien, P., Kimia, B.: Recognition of shapes by editing their shock graphs. IEEE Transaction on PAMI 26, 550–571 (2004)
Mokhtarian, F., Mackworth, A.: Scale-based description and recognition of planar curves and two-dimensional shapes. IEEE Transaction on PAMI 8, 34–43 (1986)
Belongie, S., Malik, J.: Shape matching and object recognition using shape contexts. IEEE Transaction on PAMI 24, 509–522 (2002)
Arbter, K., Snyder, W., Burhardt, H., Hirzinger, G.: Application of affine-invariant fourier descriptors to recognition of 3-d objects. IEEE Transaction on PAMI 12, 640–647 (1990)
Rivlin, E., Weiss, I.: Local invariants for recognition. IEEE Transaction on PAMI 17, 226–238 (1995)
Murase, H., Nayar, S.: Visual learning and recognition of 3-d objects from appearance. Int. J. Computer Vision 14, 5–24 (1995)
Duta, N., Sonka, M., Jain, A.: Learning shape models from examples using automatic shape clustering and procrustes analysis. LNCS. Springer, Heidelberg (1999)
Srivastava, A., Joshi, S., Mio, W., Liu, X.: Statistical shape analysis: Clustering, learning, and testing. IEEE Transaction on PAMI 27, 590–602 (2005)
Jain, A., Murty, M., Flynn, P.: Data clustering: A review. ACM Computing Surveys 31, 264–323 (1999)
Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active shape modelstheir training and application. Comput. Vis. and Imag. Underst. 61, 38–59 (1995)
Medioni, G., Yasumoto, Y.: Corner detection and curve representation using cubic b-splines. Computer Vision, Graphics and Image Processing 39, 267–278 (1987)
Mokhtarian, F., Abbasi, S., Kittler, J.: Efficient and robust shape retrieval by shape content through curvature scale space. In: Proc. First Int’l Conf. Image Database and Multi-Search, pp. 35–42 (1996)
Vanzella, W., Torre, V.: A versatile segmentation procedure. IEEE Transactions on SMC, Part B 36, 366–378 (2006)
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Daliri, M.R., Torre, V. (2006). Unsupervised Clustering of Shapes. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2006. Lecture Notes in Computer Science, vol 4291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11919476_71
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DOI: https://doi.org/10.1007/11919476_71
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