Semantic Gap Reduction in Content Based Image Retrieval: A Survey

The explosive growth of image data leads to the need of research and development of image retrieval. Content Based Image Retrieval (CBIR) is the approval image retrieval system by which the target image to be retrieved on the basis of useful features of the given image. So, in large collection, users of different domains face a problem of retrieving images relevant to the user query. To overcome this problem, different techniques are adopted. The significance of content based image retrieval system depends on the adopted features to represent images in the knowledge base. Using low-level features cannot give satisfactory results in many cases recovery; especially when high-level concepts in the user’s mind are not easily expressible in terms of low-level features, i.e. semantic gap. The need to improve the precision of image retrieval systems and reduce the semantic gap is high in view of the growing need for image retrieval. We first present semantic extraction methods, and then the key technologies for reducing the semantic gap, i.e. object-ontology, machine learning, generating semantic relevance feedback templates and web image retrieval are discussed