Content-based image retrieval (CBIR) is a difficult area of research in multimedia systems. The research has proved extremely difficult because of the inherent problems in proper automated analysis and feature extraction of the image to facilitate proper classification of various objects. An image may contain more than one objects and to segment the image in line with object features to extract meaningful objects and then classify it in high-level like table, chair, car and so on has become a challenge to the researchers in the field. The latter part of the problem, the gap between low-level features like color, shape, texture, spatial relationships and highlevel definitions of the images is called the semantic gap. Until we solve these problems in an effective way, the efficient processing and retrieval of information from images will be difficult to achieve. In this paper we explore the possibilities of how relevance feedback can help us solve this problem of semantic gap although lot of works have already been done using the concepts of relevance feedback in this area. This would enable efficient image retrieval for internet of the future.
His research interests are multimedia databases, content-based image retrieval, various indexing techniques and electronic commerce. He contributes to books on multimedia databases, attends international conferences and writes research papers for international journals. He has edited two research publications and written one book on multimedia. He currently works at the University of Central Queensland, Australia, and is involved in teaching and research works. Marquis Who's Who, a well-known publisher of biographies of people of notable achievements, has included his biography in their 7th Edition of Who's Who in Science and Engineering. In addition, his biography has been included in the 21st century issue of the 2000 Outstanding Scientists (published by International Biographical Centre, Cambridge, England in 2004) in recognition of his achievements in the field of scientific research.
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