Image Retrieval Using Navigation Pattern Mining and Relevance Feedback


Image retrieval is an important topic in the field of pattern recognition and artificial intelligence. Searching or retrieving image based on its content is called content based image retrieval. In CBIR, images are indexed by their visual content, such as color, texture, shapes. There are various methods so far implemented and all of these methods are also support by feedback system from the user to fine tune the search results. But those methods are impractical away for real applications. In this paper, novel framework method, Navigation-Pattern-based Relevance Feedback (NPRF) is used to achieve high efficiency and effectiveness of CBIR. In terms of effectiveness, the proposed search algorithm NPRFSearch 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 on RF can be achieved in a small number of feedbacks.

Cite this paper

@inproceedings{Pawar2013ImageRU, title={Image Retrieval Using Navigation Pattern Mining and Relevance Feedback}, author={Pranav M. Pawar}, year={2013} }