Abstract
Traditional image retrieval method based on global features can only extract low-level features, which are far from the semantics that human expects. So there is a huge gap between low-level features and high-level semantics. In order to overcome this gap, two approaches have been widely used: high-level semantic image representation and relevance feedback. The paper is based on region representation that comes closely to the semantics. Region-based image retrieval (RBIR) can effectively exclude the affection of backgrounds. The main work in the paper is summarized as follows: Firstly in order to solve the problems of image segment and similarity measure, an algorithm about detecting visual attended region with respect to image segment is proposed in the paper. This algorithm combined human visual attention model can effective detect the meaningful regions. And the paper proposes a similarity measure algorithm named Modified Integrated Region Matching (MIRM), which is more robust for over segmented images. Secondly this paper focuses on the applications of relevance feedback in RBIR. By studying three cases that may occur in relevance feedback respectively, this paper introduces some relevance feedback algorithms. Those are image coding and cluster based relevance feedback algorithm, Mapping Convergence (MC) based relevance feedback algorithm, Adaptive Boosting (AdaBoost) Support Vector Machines (SVM) based relevance feedback algorithm, Asymmetric Bagging SVM based relevance feedback algorithm and region representation based SVM relevance feedback algorithm. A large number of experimental results demonstrate that all the algorithms, both region based image representation and relevance feedback, proposed in this paper can improve the performance and efficiency of image retrieval.