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Image Clustering with Metric, Local Linear Structure, and Affine Symmetry

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNCS,volume 3021)

Abstract

This paper addresses the problem of clustering images of objects seen from different viewpoints. That is, given an unlabelled set of images of n objects, we seek an unsupervised algorithm that can group the images into n disjoint subsets such that each subset only contains images of a single object. We formulate this clustering problem under a very broad geometric framework. The theme is the interplay between the geometry of appearance manifolds and the symmetry of the 2D affine group. Specifically, we identify three important notions for image clustering: the L 2 distance metric of the image space, the local linear structure of the appearance manifolds, and the action of the 2D affine group in the image space. Based on these notions, we propose a new image clustering algorithm. In a broad outline, the algorithm uses the metric to determine a neighborhood structure in the image space for each input image. Using local linear structure, comparisons (affinities) between images are computed only among the neighbors. These local comparisons are agglomerated into an affinity matrix, and a spectral clustering algorithm is used to yield the final clustering result. The technical part of the algorithm is to make all of these compatible with the action of the 2D affine group. Using human face images and images from the COIL database, we demonstrate experimentally that our algorithm is effective in clustering images (according to ojbect identity) where there is a large range of pose variation.

Keywords

  • Cluster Algorithm
  • Cluster Result
  • Image Space
  • Quotient Space
  • Spectral Cluster

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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© 2004 Springer-Verlag Berlin Heidelberg

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Lim, J., Ho, J., Yang, MH., Lee, Kc., Kriegman, D. (2004). Image Clustering with Metric, Local Linear Structure, and Affine Symmetry. In: Pajdla, T., Matas, J. (eds) Computer Vision - ECCV 2004. ECCV 2004. Lecture Notes in Computer Science, vol 3021. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24670-1_35

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  • DOI: https://doi.org/10.1007/978-3-540-24670-1_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-21984-2

  • Online ISBN: 978-3-540-24670-1

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