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The features employed i n c ontent-based r etrieval are most often simple low-level representations, while a human observer judges similarity between images based on high-level semantic properties. Using textures as an example, we show that a more a c curate description of the underlying distribution of low-level features does not improve the retrieval performance. We also introduce the simpli ed multiresolution symmetric autoregressive model for textures, and the Bhattacharyya distance baseddoi:10.1109/icpr.2000.902912 dblp:conf/icpr/XuGMC00 fatcat:ftiwm5nlfrf2vktbyei7udp4we