期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2015
卷号：6
期号：2
页码：1665-1668
出版社：TechScience Publications
摘要：Content Based Image Retrieval (CBIR) is commonly used in image processing. But the accuracy of CBIR system is not very high. To improve the performance of CBIR system Relevance feedback system can be used. In relevance feedback system the user refines the search results progressively by marking images in the results as "relevant", "irrelevant", or "neutral" to the search query and then repeating the search with the new information. In many cases there may be large number of images to label. Most of the times user would not like to label a large number of images. So in this paper we are using semi-supervised method. This means the user needs to label only few most informative images. These labeled images are then used as training set for SVM classifier. Then images in database are resorted based on new similarity metric. If the user is satisfied with the results, Relevance feedback is no longer required and the system gives the final results. These results are most semantically relevant to the query image. Thus the image retrieval process is ended. Otherwise, Relevance Feedback will be performed iteratively
