Improved CBIR using Multileveled Block Truncation Coding

The paper presents improved content based image retrieval (CBIR) techniques based on multilevel Block truncation coding using multiple threshold values. Block 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. The color averaging methods used here are BTC Level-1, BTC Level-2, BTC Level-3.Here the feature vector size per image is greatly reduced by using mean of each plane and find out the threshold value then divide each plane using threshold value, color averaging is applied to calculate precision and recall to calculate the performance of the algorithm. Instead of using all pixel data of image as feature vector for image retrieval, these six feature vectors can be used, resulting into better performance and if increased the no of feature vector get better performance .The proposed CBIR techniques are tested on generic image database having 1000 images spread across 11 categories. For each proposed CBIR technique 55 queries (5 per category) are fired on the generic image database To compare the performance of image retrieval techniques average precision and recall are computed of all queries. The results have shown the performance improvement (higher precision and recall values) with proposed methods compared to BTC Level-1.