期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2014
卷号：2
期号：7
出版社：S&S Publications
摘要：The significance of content based image retrieval system (CBIR) depends on the adopted features torepresent images in the knowledge base. Using low-level features cannot give satisfactory results in many casesrecovery; especially when high-level concepts in the user‟s mind are not easily expressible in terms of low-levelfeatures, ie semantic gap. Semantic gap between visual features and human semantics has become a bottleneck incontent-based image retrieval. The need to improve the precision of image retrieval systems and reduce the semanticgap is high in view of the growing need for image retrieval.In this paper, first introduce semantic extraction methods, and then the key technologies for reducing the semantic gap,ie, object-ontology, machine learning, generating semantic relevance feedback templates and web image retrieval arediscussed.
