Image retrieval using multi-granularity color features

Making full use of image information with its own to extract features is the crucial problem in content based image retrieval (CBIR). In this paper, we import quotient space granularity computing theory into image retrieval field, clarify the granularity thinking in image retrieval, and a novel image retrieval method is proposed. Firstly, aiming at the different behaviors under different granularities, obtain color features under different granularities, achieve different quotient spaces; secondly, do the attribute combination to the obtained quotient spaces according to the quotient space granularity combination principle; and then realize image retrieval using the combined attribute function. Comparing with methods adopting single attribute feature the image retrieval method based on quotient space granularity combination utilizes the image information with its own in a more effective way. The experimental results demonstrate the feasibility and validity of the proposed method.