Nowadays, virtually all spheres of human life including commerce, government, academics, hospitals, crime prevention, surveillance, engineering and historical research use information as images, so the volume of digital data is increasing rapidly. These images and their data are categorized and stored on computers and the problem appears when retrieving these images from storage media. Thus, Content based image retrieval from large resources has become an area of wide interest in recent years.

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... n this paper an efficient general-purpose CBIR system that uses color, texture and shape as visual features to describe the content of an image is proposed. The main contribution of my work is of three directions. First, the feature vector of the image is extracted by calculating the color coherence vector (CCV) which defines degree to which pixels of that color are members of large similarly colored regions, discrete cosine transform (DCT) is used to extract texture features, which expresses a finite sequence of data points in terms of a sum of cosine functions oscillating at different frequencies and edge histogram features that include four categories is used as shape descriptors which are invariant to rotation and scaling. Second, to speed up retrieval and similarity computation of the proposed system, ant colony optimization (ACO) technique is used to optimize the features. Third, to improve the efficiency of the system, relevance feedback using support vector machine (SVM) is used.