Nowadays, Content-Based Image Retrieval has been the mainstay of image retrieval both in fields of research and application. To attain optimal retrieval results, relevance feedback (RF) methods are incorporated into CBIR by taking user's feedbacks into account. However, explicit RF methods rely heavily on active user engagement during search sessions, which is unrealistic in real applications. This paper presents an implicit RF method, Preference Estimation-based RF (PERF) for CBIR. PERF

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... s implicit user browsing histories to build a user preference model. The model will be refined iteratively and used to train a preference classifier for the user. In addition, an adaptive mechanism is adopted to realize the personalization of preference model. Our proposed method is tested and the experimental results reveal that PERF can achieve good retrieval precision with scarce explicit engagement from users. Index Terms-CBIR, relevance feedback, implicit, browsing behaviors, preference model, adaptive mechanism.