Abstract
In this paper, we propose an efficient technique for content based image retrieval which uses the local color and texture features of the image. Firstly the image is divided into sub blocks of equal size. The color and texture features of each sub-block are computed. Color of each sub-block is extracted by quantifying the HSV color space into non-equal intervals and the color feature is represented by cumulative histogram. Texture of each sub-block is obtained by using gray level co-occurrence matrix. An integrated matching scheme based on Most Similar Highest Priority principle is used to compare the query and target image. The adjacency matrix of a bipartite graph is formed using the sub-blocks of query and target image. This matrix is used for matching the images. Euclidean distance is used in retrieving the similar images. The efficiency of the method is demonstrated with the results.
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