Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320–088X IJCSMC, Vol. 2, Issue. 5, May 2013, pg.155 – 161 RESEARCH ARTICLE © 2013, IJCSMC All Rights Reserved 155 Content Based Image Retrieval with Log Based Relevance Feedback Using Combination of Query Expansion and Query Point movement Bhailal Limbasiya 1 , Swati Patel 2 1 Department of Computer Science & Technology, Gujarat Technological University, Ahmedabad, India 2 Assistance professor, L. D. College of Engineering, Gujarat Technological University, Ahmedabad, India 1 bhailal.ldce@gmail.com; 2 swati.ldce@gmail.com Abstract— This paper presents the Log based relevance feedback techniques, which combines two popular techniques of relevance feedback: query point movement and query expansion. From the past experiments, these two techniques are giving good results for image retrieval. But query point movement is limited by a constraint of unimodality in taking into account the user feedbacks. Query expansion gives better results than query point movement, but it cannot take into account irrelevant images from the user feedbacks. We combine the two techniques to profit from their advantages and to cope with their limitations. From a single point initial query, query expansion provides a multiple point query, which is then enhanced using query point movement. To learn the multiple point queries, the irrelevant feedback images are classified into query points which are clustered from relevant images using the query expansion technique. The experiments show that our method gives better results in comparison with the two techniques of relevance feedback taken individually. Key Terms: - Relevance feedback; Query expansion; Query point movement; Log-based relevance feedback I. INTRODUCTION Content based image retrieval (CBIR) has received much attention in the last decade, which is motivated by the need to efficiently handle the rapidly growing amount of multimedia data. Content based image retrieval is the technologies that retrieve images from a very large data base by their low level visual features such as color, texture and shape. It covers versatile areas, such as image segmentation, image feature extraction, representation, mapping of features to semantics, storage and indexing, image similarity-distance measurement and retrieval making CBIR system development a challenging task. Many CBIR systems have been developed, including QBIC [1], Photobook [2], MARS [3], NeTra [4], PicHunter [5], Blobworld [6], VisualSEEK [2], SIMPLIcity [7]. Many researchers in information-technology field and leading academic institutions try to develop content based image retrieval system for very large image database. Recently researcher focus in CBIR has moved to an interactive mechanism called Relevance feedback that involves a human as part of the retrieval process.[8],[4] In this approach, the retrieval process is interactive. To search for desirable images, a user provides the query image, and the system returns a set of similar images based on the extracted features. In CBIR systems with relevance feedback (RF), a user can mark returned images, which are then fed back into the systems as a new refined query for the next round of retrieval. Given the difficulty in learning the users’ information needs from their feedback, multiple rounds of relevance feedback are usually required before satisfactory results are achieved. As a result, the relevance feedback phase can be extremely time-consuming. Moreover, the procedure of specifying the relevance of images in relevance feedback is usually viewed as a tedious and boring step by most users. Hence, it is required for a CBIR system