Using Relevance Feedback in Bridging Semantic Gaps in Content-Based Image Retrieval

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 high-level 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.