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Image Similarity Based on Hierarchies of ICA Mixtures
[chapter]
Independent Component Analysis and Signal Separation
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
doi:10.1007/978-3-540-74494-8_98
dblp:conf/ica/SerranoSIV07
fatcat:l6ablkxlz5hffk3yisykvy64zy