期刊名称：International Journal of Computer Technology and Applications
电子版ISSN：2229-6093
出版年度：2012
卷号：3
期号：4
页码：1357-1364
出版社：Technopark Publications
摘要：Image retrieval based on image content has become a hot topic in the field of image processing and computer vision. Content-based image retrieval (CBIR) is the basis of image retrieval systems. Unlike traditional database queries, content-based multimedia retrieval queries are imprecise in nature which makes it difficult for users to express their exact information need in the form of a precise right query. 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. This paper studies about the research on ways to extend and improve query methods for image databases is widespread, we have developed the QBIC (Query by Image Content) system to explore content-based retrieval methods. To achieve the high efficiency and effectiveness of CBIR we are using two type of methods for feature extraction like SVM (support vector machine)and NPRF(navigation-pattern based relevance feedback). By using SVM classifier as a category predictor of query and database images, they are exploited at first to filter out irrelevant images by its different low-level, concept and key point-based features. Thus we may reduce the size of query search in the data base then we may apply NPRF algorithm and refinement strategies for further extraction. In terms of efficiency, the iterations of feedback are reduced by using the navigation patterns discovered from the user query log. In terms of effectiveness, the 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 on RF can be achieved in a small number of feedbacks.
