Efficient Relevance Feedback for Content- based Image Retrieval by Mining User Navigation Patterns

Abstract—Nowadays, content-based image retrieval (CBIR) is the mainstay of image retrieval systems. To be more profitable, relevance feedback techniques were incorporated into CBIR such that more precise results can be obtained by taking user ’s feedbacks into account. However, existing relevance feedback-based CBIR methods usually request a number of iterative feedbacks to produce refined search results, especially in a large-scale image database. This is impractical and inefficient in real applications. In this paper, we propose a novel method, NPRF (Navigation Pattern-based Relevance Feedback), to achieve the high efficiency and effectiveness of CBIR in coping with the large-scale image data. In terms of efficiency, the iterations of feedback are reduced substantially by using the navigation patterns discovered from the user query log. In terms of effectiveness, our proposed search algorithm NPRFSearch makes use of the discovered navigation patterns and three kinds of query refinement strategies, QPM (Query-Point-Movement), QR (Query-Reweighting) and QEX (Query-Expansion), to converge the search space towards the user’s intention effectively. By using NPRF method, high quality of image retrieval on RF can be achieved in a small number of feedbacks. The experimental results reveal that NPRF outperforms other existing methods significantly in terms of precision, coverage and number of feedbacks. Index Terms—content-based image retrieval, relevance feedback, query point movement, query expansion, navigation pattern mining