期刊名称：International Journal of Electronics, Communication and Soft Computing Science and Engineering
印刷版ISSN：2277-9477
出版年度：2015
卷号：4
期号：Special 2
出版社：IJECSCSE
摘要：The performance of the CBIR system can be improved by reducing the semantic gap between visual features and human semantics. Relevance Feedback (RF) approach refines the retrieval process with users feedback on Content based image retrieval (CBIR) system results. A variety of Relevance Feedback (RF) methods have been widely used to reduce the semantic gap. It was observed that existing Relevance Feedback techniques face the challenges of number of iterations and the execution time. To improve the retrieval efficiency of the existing system, the proposed RF approach uses classification based method. The positive and negative examples provided by the user will be used for the classification. A binary classifier will be trained to distinguish between relevant and irrelevant images according to the preferences of the user. The trained classifier will be later used to provide an updated ranking of the database images represented in the space of the selected features
