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Color-Texture Feature based Image Retrieval using DCT applied on Kekre’s Median Codebook

H. B. Kekre, Tanuja K. Sarode, Sudeep D. Thepade


Image retrieval has become imperative area of research because of vide range of applications needing the image data search facility. Most of the research approaches in the area are either database based indexing or image processing based CBIR. The hours need is to combine these parallel going approaches of research to have better image retrieval techniques. The paper proposes a novel technique for image retrieval using the color-texture features extracted from images based on the color indexing using vector quantization. This gives better discrimination capability for CBIR. Here we are dividing the database image into 2x2 pixel windows to obtain 12 color descriptors (Per pixel Red, Green and Blue) per row of window table. Then the Kekre’s Median Codebook Generation (KMCG) is applied on window table to get 256 centre rows. The DCT is applied on this centre row vector to obtain feature set of size 256x12, which is user for image retrieval. The method takes fewer computations as compared to conventional DCT applied on complete image. The method gives the color-texture features of the image database at reduced feature set size.


CBIR, Data Compression, Vector Quantization, Color-Texture Features

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