An Adaptive-Weight Hybrid Relevance Feedback Approach For Content Based Image Retrieval

Content-based image retrieval (CBIR) has been receiving intensive research attention for many applications. In order to provide the users with more precise retrieval results, relevance feedback (RF) methods have been incorporated into CBIR which take the user's feedbacks into account. In general, explicit RF methods demand too much user effort while implicit RF methods suffer from lower retrieval accuracy. As such, we propose a hybrid RF method, adaptive-weight hybrid relevance feedback (AHRF) for content-based image retrieval. AHRF integrates explicit user grading and implicit user browsing histories to build a user preference model. The model is refined iteratively and used to train a preference classifier for the users. Moreover, an adaptive-weight mechanism is proposed to achieve a personalized preference model. Our proposed method is tested on a subset of the Corel Database and the experimental results reveal that AHRF can achieve good retrieval precision with less user effort.