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Content-based image retrieval (CBIR) is one of the most active research areasin multimedia information retrieval. Given a query image, the task is to searchrelevant images in a repository. Low level features like color, texture, andshape feature vectors of an image are always considered to be an importantattribute in CBIR system. Thus the performance of the CBIR system can beenhanced by combining these feature vectors. In this paper, we propose a novelCBIR framework by applying to index using multiclass SVM and finding theappropriate weights of the individual features automatically using therelevance ratio and mean difference. We have taken four feature descriptors torepresent color, texture and shape features. During retrieval, feature vectorsof query image are combined, weighted and compared with feature vectors ofimages in the database to rank order the results. Experiments were performed onfour benchmark datasets and performance is compared with existing techniques tovalidate the superiority of our proposed framework.

Automatic Feature Weight Determination using Indexing and Pseudo-Relevance Feedback for Multi-feature Content-Based Image Retrieval