A Review on Different Content Based Image Retrieval Techniques Using High Level Semantic Features
|Nancy Goyal1, Navdeep Singh2
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The significance of content based image retrieval system (CBIR) 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, ie semantic gap. Semantic gap between visual features and human semantics has become a bottleneck in content-based image retrieval. 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. 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 are discussed.