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Unsupervised Clustering of Shapes

  • Mohammad Reza Daliri
  • Vincent Torre
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4291)

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....

Keywords

IEEE Transaction Unsupervised Cluster Shape Context Procrustes Analysis Curvature Scale Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mohammad Reza Daliri
    • 1
  • Vincent Torre
    • 1
  1. 1.SISSATriesteItaly

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