A Framework of CBIR System Based on Relevance Feedback

Content-based image retrieval (CBIR) is an effective approach for obtaining desired image, however, due to the semantic gap between low-level visual features and high-level concept of image, CBIR system of state-of-the-art always can't achieve satisfying retrieval performance. In this paper, we propose a novel CBIR system framework. In order to bridge the semantic gap, the mechanism of relevance feedback is involved in the system. More various features are included at low level, which can provide more abundant image content description. A bi-coded chromosome based genetic algorithm is performed to obtain optimal features and relevant optimal weights based on users' relevance feedback. With the optimal feature set and optimal weights, the similarity between image in original searching results and query image is considered to be the main factor of rank score.