Overview of research on finding semantic meanings from low-level features in content-based image retrieval

Content-based image retrieval is a bottleneck 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. Until we win over these challenges, the efficient processing and retrieval of information from images will be difficult to achieve. In this paper we take stock of the current situation and suggest some future directions in the resolution of the problem of extracting high-level definitions from low-level features like color, texture, shape and spatial relations.