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Color Based Image Retrieval using Amendment of Block Truncation Coding with YCbCr Color Space
Block Truncation Coding (BTC) based features is one of the CBIR methods proposed using color features of image. The approach basically considers red, green and blue planes of image together to compute feature vector. Here we have boosted this BTC based CBIR as BTC-YCbCr and Spatial BTC- YCbCr. Here YCbCr color space is considered. In BTC- YCbCr feature vector is computed by considering Y, Cb and Cr planes of the image independently. While in Spatial BTC- YCbCr , the feature vector is composed of four parts. Each part is representing the features extracted from one of the four non overlapping quadrants of the image. The new proposed methods are tested on the database of 1000 images and the results show that the precision is improved in Spatial BTC- YCbCr and recall is better in BTC- YCbCr. If both precision and recall are considered together Spatial BTC- YCbCr outperforms the other methods discussed in the paper.
CBIR, BTC, RGB Color Space, YCbCr Color Space, Spatial BTC.
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