Abstract
Due to the development of images data production and use, the size of images databases is exponentially increased recently. Consequently, the similarity search time in images databases becoming a severe problem which affects the exploitation of image data. In this paper, we address the problem of reducing the similarity search cost in large, high-dimensional and dynamic image datasets; we propose a coherent improvement of the D-index method, the amelioration aims to reduce the number of distances computed during a query search. The method is tested over a 112 dimensional color images databases, the experiences show that the proposed D-index version has proved a good performance in comparison with the classical D-index.
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