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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Serrano, A., Salazar, A., Igual, J., & Vergara, L. (2007). Image similarity based on hierarchies of ICA mixtures. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4666 LNCS, pp. 786–793). Springer Verlag. https://doi.org/10.1007/978-3-540-74494-8_98
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