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ECCV
2004
Springer

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

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Image Clustering with Metric, Local Linear Structure, and Affine Symmetry
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 L2 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 ar...
Jongwoo Lim, Jeffrey Ho, Ming-Hsuan Yang, Kuang-Ch
Added 15 Oct 2009
Updated 15 Oct 2009
Type Conference
Year 2004
Where ECCV
Authors Jongwoo Lim, Jeffrey Ho, Ming-Hsuan Yang, Kuang-Chih Lee, David J. Kriegman
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