ICA 2007

ICA 2007
7th International Conference on
Independent Component Analysis
and Signal Separation

London, UK        9 - 12 September 2007

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Paper No: 147

Image Similarity based on Hierarchies of ICA Mixtures

Author(s): Arturo Serrano, Addisson Salazar, Jorge Igual, Luis Vergara

Abstract

This paper presents a novel algorithm to build hierarchies from independent component analyzer mixtures and its application to image similarity measure. The hierarchy algorithm composes an agglomerative (bottom-up) clustering from the estimated parameters (basis vectors and bias terms) of the ICA mixture. Merging at different levels of the hierarchy is made using the Kullback-Leibler distance between clusters. The procedure is applied to merge similar patches on a natural image, to group different images of an object, and to create hierarchical levels of clustering from images of different objects. Re-sults show suitable image hierarchies obtained by clustering from basis func-tions to higher-level structures.

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