2013 International Conference on Advanced Computing and Communication Systems (ICACCS -2013), Dec. 19 – 21, 2013, Coimbatore, INDIA Content Based Image Retrieval System using Texture and Modified Block Truncation Coding Purohit Shrinivasacharya Department of Information Science and Engineering Siddaganga Institute of Technology Tumkur, India purohitsn@gmail.com Dr. M. V Sudhamani Department of Information Science and Engineering R. N. S Institute of Technology Bengalore, India mvsudha_raj@hotmail.com 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 x 4, 8 x 8, 16 x 16, 32 x 32, 64 x 64, 128 x 128 and 256 x 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. Keywords—BTC; energy; database; color; CBIR; speed I. INTRODUCTION The amount of image data is growing very fast in the present day, because of huge bandwidth and large volume of storage for the little price. This creates a problem of retrieving the stored image in the system, because extracting the image with the surrounding textual information is very difficult and it won't give the best result. Hence the present word is required the different method to extract the stored images i.e CBIR. The recent CBIR research tries to combine both of the Text Based Image Retrieval and CBIR methods and has developed an efficient image representation and data structures, query processing algorithms, intelligent query interfaces and domain independent system architecture [1]. Implementing the CBIR system is very difficult because extracting the visual property of an image is a tedious job. Most of the researchers are finding the methods to extract the good visual features from the images that have to take less time and less space to store this image features. The major aim of any image retrieval system is to retrieve as many images as possible from an image database such that these retrieved images meet the user’s requirements. The user’s can be specified the requirements in the form of an image or a sketch or a major color or keywords depending on type of system. An image retrieval system provides the user with a way to access, browse and retrieve efficiently and possibly in real time, form these databases [2]. Image retrieval systems can be divided into two main types: Text Based Image Retrieval and Content Based Image Retrieval. In the early years Text Based Image Retrieval was popular, but nowadays Content Based Image Retrieval has been a topic of intensive research [3] [4]. In this paper we are presenting a CBIR technique to retrieve the images from the local database or from WWW. The proposed method uses the texture analyzed [5] and gray color information for extracting the features of the image for later comparison. The texture information energy, contrast, entropy and uniformity are extracted from the Gray-Level Co- occurrence Matrix (GLCM) [6, 7] statistical approaches. The gray color features are extracted by using the modified BTC [8] method. The different category images are chosen for experiment to test the proposed system performance. The Fig. 1. Shows the different categories of images used. [978-1-4799-3506-2/13/$31.00 ©2013 IEEE] Fig. 2. Proposed CBIR System