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We propose an approach based on the fusion of multiple search strategies to content-based texture retrieval. Given the complexity of images and users’ needs, there is no model or system which is the best than all the others in all cases and situations. Therefore, the basic idea of multiple search strategies is to use several models, several representations, several search strategies, several queries, etc. and then fuse (merge) the results returned by each model, representation, strategy or query in a unique list by using appropriate fusion models. Doing so, search effectiveness (relevance) should be improved without necessarily altering, in an important way, search efficiency. We consider the case of homogeneous textures. Texture is represented by three (3) models/viewpoints. We consider also the special case of invariance and use both multiple representations and multiple queries to address this difficult problem. Benchmarking carried out on two (2) image databases show that retrieval relevance (effectiveness) is improved in a very appreciable way with the fused model.

TEXTURE REPRESENTATION AND RETRIEVAL BASED ON MULTIPLE STRATEGIES