Content based image retrieval system using texture and modified block truncation coding

Abstract

According to the recent literature, it is pragmatic that image storage and retrieval through the World Wide Web (WWW) has made impressive progress. Practical searching for image still confronts us with present retrieval systems. A Content Based Image Retrieval (CBIR) system provides an efficient way of retrieving related images from image collections. In this paper we present a new technique to extract the images from the database to achieve better performance and efficiency. The novel method uses an approach, which combines texture information and modified Block Truncation Coding (BTC) method to extract the color features from the image. The whole image is divided into a fixed block of size 4 × 4, 8 × 8, 16 × 16, 32 × 32, 64 × 64, 128 × 128 and 256 × 256 non overlapped blocks. For each block, we calculated the threshold as a mean of the pixel values and the number of pixels which are lower than threshold value and upper than the threshold value are counted and mean of these lower and upper values have been taken and these two are considered as features. Now this process is repeated for the all blocks to extract the features of the image. The second step will compute the four texture features using the Gray-Level Co-occurrence Matrix (GLCM) as a statistical approach to extract energy, contrast, entropy and uniformity. We have used this modified BTC and texture features of the image to retrieve images. Finally, we have conducted experiments on a ground truth image database that has 1000 images of different categories. A different block sizes have been used to demonstrate the experimental results and compared with tabulation. From this we have observed that the performance is better and efficiency is low with smaller block sizes and vice versa with higher block sizes. So we have to choose the middle ranges of block sizes for better performance with better efficiency. As we have observed from the results that the performance of the retrieval system is improved because of combination of texture and color features. The proposed system's performance has been improved by an average precision of 2.41% and recall of 8.22% when compared to existing systems.