  Content-based image retrieval (CBIR) is one of the most active research areas
in multimedia information retrieval. Given a query image, the task is to search
relevant images in a repository. Low level features like color, texture, and
shape feature vectors of an image are always considered to be an important
attribute in CBIR system. Thus the performance of the CBIR system can be
enhanced by combining these feature vectors. In this paper, we propose a novel
CBIR framework by applying to index using multiclass SVM and finding the
appropriate weights of the individual features automatically using the
relevance ratio and mean difference. We have taken four feature descriptors to
represent color, texture and shape features. During retrieval, feature vectors
of query image are combined, weighted and compared with feature vectors of
images in the database to rank order the results. Experiments were performed on
four benchmark datasets and performance is compared with existing techniques to
validate the superiority of our proposed framework.
