Image retrieval using augmented block truncation coding techniques

With the tremendous growth of ICT (Information and Communication Technology), we are able to generate, store, share and transfer enormous amount of information. World Wide Web have further made is easy to access the information anytime, anywhere in the world. With the advent of high capacity communication links and storage devices even most of the information generated is of multimedia in nature. Images have major share in this information and the number of image achieves are growing with the jet speed Just having the tremendous amount of information is not useful unless we do not have the methodologies to effectively search the related data from it in minimum possible duration. The relativity of the image data is application specific. Here to search and retrieve the expected images from the database we need Content Based Image Retrieval (CBIR) system. CBIR extracts the features of query image and try to match them with the extracted features of images in the database. Then based on the similarity measures and threshold the best possible candidate matches are given as result. There have been many approaches to decide and extract the features of images in the database. Binary truncation Coding 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 augmented this BTC based CBIR as BTC-RGB and Spatial BTC-RGB. In BTC-RGB feature vector is computed by considering red, green and blue planes of the image independently. While in Spatial BTC-RGB, 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 1000 images database and the results show that the precession is improved in BTC-RGB and is even better in Spatial BTC-RGB.

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