Digital Image Retrieval Using Intermediate Semantic Features and Multistep Search

Recently, semantic image retrieval has attracted large amount of interest due to the rapid growth of digital image storage. However, existing approaches have severe limitations. In this paper, a new approach to digital image retrieval using intermediate semantic features and multistep search has been proposed. Instead of looking for human level semantics which is too challenging at this stage, the research looks for heuristic information and intermediate semantic features which can describe image content objectively. Different from the conventional approaches, the intermediate features are used as filters to eliminate large amount of irrelevant images. Conventional content based image retrieval techniques and relevance feedback (RF) are applied following the filtering to improve the retrieval accuracy. The proposed system has the power of capturing both regional features and global features, and making use of both semantic features and low level features. The proposed system also uses...

• Type: Conference
• Year: 2008
• Where: DICTA
• Authors: Dengsheng Zhang, Ying Liu, Jin Hou
• Keywords: Applied Computing, DICTA 2008, Image Retrieval, Intermediate Semantic Features, Semantic Features