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Nowadays, having effective methods for accessing the desired images is essential with the huge amount of digital images. The aim of this paper is to build a meaningful mid-level representation of visual documents to be used later for matching between the query image and other images in the desired database. The approach is based firstly on constructing different visual words using local patch extraction and fusion of descriptors. Then, we represent the spatial constitution of an image as a mixture of n Gaussians in the feature space. Finally, we extract different association rules between frequent visual words in the local context of the image to construct visual phrases. Experimental results show that our approach outperforms the results of traditional image retrieval techniques.

USING ASSOCIATION RULES AND SPATIAL WEIGHTING FOR AN EFFECTIVE CONTENT BASED-IMAGE RETRIEVAL