DISTRIBUTED CONTENT-BASED IMAGE RETRIEVAL WITH HIGH-LEVEL SEMANTICS

The accuracy and the performance of a content-based image retrieval (CBIR) system is inherently constrained by the features adopted to represent the images in the database . Use of low-level features can not give satisfactory retrieval results in many cases especially when the high-level concepts in the user s mind is not easily expressible in terms of the low-level features. So, In order to improve the retrieval accuracy and the performance of a content-based image retrieval (CBIR) system, research focus has been shifted from designing sophisticated low-level feature extraction algorithms to reducing the semantic gap between the visual features and the richness of human semantics. This paper attempts to provide new idea in high-level semantic-based image retrieval. Major recent publications are included in this paper covering different aspects of the research in this area, including low-level image feature extraction, similarity measurement, and how deriving high-level semantic features. This paper identify two techniques in narrowing down the semantic gap using object ontology to define high-level concepts; using relevance feedback to learn users intention