Implementation of Relevance Feedback for Content Based Image Retrieval using Image Mining User Navigation Pattern

Mrunmayi Joshi, Supriya A. Kinariwala, Lalita B. Randive


Image retrieval is the basic requirement task in the present scenario. Nowadays, content-based image retrieval (CBIR) is the mainstay of image retrieval systems in which the target image to be retrieved based on the useful features of the given image. Image mining is the arising concept which can be used to extract potential information from the general collection of images. This paper proposes a new method, Navigation-Pattern-based Relevance Feedback (NPRF) image mining which is implemented in MATLAB, to achieve the preciseness of results and effectiveness of CBIR even when large-scale image data is present. In terms of preciseness, feedback iterations are reduced in substantial amount by using the navigation patterns discovered from the user query log. In terms of effectiveness, our proposed search algorithm NPRF Search makes use of the discovered navigation patterns and three kinds of query refinement strategies, Query Point Movement (QPM), Query Reweighting (QR), and Query Expansion (QEX), to converge the search space toward the user‟s intention effectively. By using NPRF method, high quality of image retrieval 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.

Keywords: Image Mining, Feature Extraction, Query Point Movement, Query Reweighting, Query Expansion, Feedback

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