Vol 04, Special Issue01; 2013 PUBLICATIONS OF PROBLEMS & APPLICATION IN ENGINEERING RESEARCH - PAPER http://ijpaper.com/ CSEA2012 ISSN: 2230-8547; e-ISSN: 2230-8555 2010-2013 - IJPAPER Indexing in Process - EMBASE, EmCARE, Electronics & Communication Abstracts, SCIRUS, SPARC, GOOGLE Database, EBSCO, NewJour, Worldcat, DOAJ, and other major databases etc., 247 RELEVANCE FEEDBACK FOR CONTENT BASED IMAGE RETRIEVAL BASED ON MULTITEXTON HISTOGRAM AND MICROSTRUCTURE DESCRIPTOR KRANTHI KUMAR.K #1 , T.VENU GOPAL* 2 , M. RAMA KRISHNA #3 Assistant Professor, Department of IT, SNIST, Yamnampet, Ghatkesar, Hyderabad, AP, INDIA Associate Professor, Department of CSE, JNTUH College of Engineering, Kondagattu, Karimnagar, AP, INDIA Assistant Professor, Department of CSE, SNIST, Yamnampet, Ghatkesar, Hyderabad, AP, INDIA kranthikathula@gmail.com , t_vgopal@rediffmail.com , ramakrishna.miryala@gmail.com ABSTRACT Image retrieval is an important topic in the field of pattern recognition and artificial intelligence. There are three categories of image retrieval methods: text-based, content-based and semantic-based. In CBIR, images are indexed by their visual content, such as color, texture, shapes. A new image feature detector and descriptor, namely the micro-structure descriptor [1] (MSD) is discussed to describe image features via micro-structures. The micro-structure is defined based on the edge orientation similarity, and the MSD is built based on the underlying colors in micro-structures with similar edge orientation. Content-based image retrieval (CBIR) is the mainstay of image retrieval systems. To be more profitable relevance feedback techniques are incorporated into CBIR such that more precise results can be obtained by taking user‟s feedbacks into account. The semantic gap between low-level features and high-level concepts handled by the user is one of the main problems in image retrieval. On the other hand, the relevance feedback has been used on many CBIR systems such as an effective solution to reduce the semantic gap. The gap is reduced by using the Multitexton Histogram descriptor [2]. In this paper, a novel framework method called Relevance Feedback is used to achieve high efficiency and effectiveness of CBIR in coping with the large-scale image data. For that reason this paper proposes a method of relevance feedback based on Multitexton Histogram descriptor to represents the effective feature representations, and the Microstructure descriptor (MSD) for efficient feature extraction of an image. By using this method, high quality of image retrieval on Relevance Feedback can be achieved in a small number of feedbacks. In terms of efficiency, iteration of feedback is reduced substantially by using the navigation patterns discovered from the user query log, which reduce the computational processing time. Keywords--- CBIR, Relevance Feedback, Semantic Gap, Microstructure descriptor (MSD), Multitexton Histogram descriptor. I. INTRODUCTION Images and graphics are among the most important media formats for human communication and they provide a rich amount of information for people to understand the world. In many areas of commerce, government, academia, and hospitals, large collections of digital images are being created. Many of these collections are the product of digitizing existing collections of analogue photographs, diagrams, drawings, paintings, and prints. Usually, the only way of searching these collections was by keyword indexing, or simply by browsing. Digital image databases however, open the way to content based searching. CBIR [3] system is required to help retrieve images based on visual properties such as color, texture or pictorial entities such as shape of an object. The primary goal of the CBIR system is to construct meaningful descriptions of physical attributes from images to facilitate efficient and effective retrieval. The primary goal of the CBIR system is to construct meaningful descriptions of physical attributes from images to facilitate efficient and effective retrieval. In recent years, digital imaging has experienced tremendous growth in the world and it tends to increase exponentially. A way to retrieve this information is through the CBIR systems, although a large amount of research has been developed in this field, the performance in this system has not yet been successful due to the existence of semantic gap. Because of we use high-level concepts such as keywords or text descriptions to describe images content and/or to measure their similarity. While that the computer-vision systems can automatically extracted low-level features from images such as color, shape, texture and spatial relationships. On the other hand there is also subjectivity of human perception of visual content of the images because of different persons or the same person under different circumstances, may perceive the same visual content differently during the information retrieval process [4], [5]. In that sense the CBIR systems try to reduce the semantic gap through relevance feedback based on the event activated human computer interaction model, which is a challenging task. The relevance feedback (RF), adapts the response of a system according to the relevant information fed back to it so that the adjusted response is a better approximation to the users information needs [6]. While there is much research effort addressing content-based image retrieval issues, the performance of content- based image retrieval methods are still limited, especially in the two aspects of retrieval accuracy and response