Image similarity based on hierarchies of ICA mixtures

Authors:
Arturo Serrano;Addisson Salazar;Jorge Igual;Luis Vergara
Affiliations:
Universidad Politécnica de Valencia, Departamento de Comunicaciones, Valencia, Spain;Universidad Politécnica de Valencia, Departamento de Comunicaciones, Valencia, Spain;Universidad Politécnica de Valencia, Departamento de Comunicaciones, Valencia, Spain;Universidad Politécnica de Valencia, Departamento de Comunicaciones, Valencia, Spain
Venue:
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Year:
2007

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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. Results show suitable image hierarchies obtained by clustering from basis functions to higher-level structures.