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13 January 2012 Research of image retrieval system framework based on ontology and content
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The current most desirable image retrieval feature is retrieving images based on their semantic content. In order to improve the retrieval accuracy of content-based image retrieval systems, 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. In this paper, we put forward a system framework of image retrieval based on content and ontology, which has the potential to fully describe the semantic content of an image, allowing the similarity between images and retrieval query to be computed accurately. In the system, we identify third major categories of techniques in narrowing down the "semantic gap": (1) using object ontology to define high-level concepts; (2) using machine learning methods to associate low-level features with query concepts; (3) using ontology reasoning to extend image retrieval. Finally, the paper does some testing experiment, whose result shows the feasibility of the system framework.
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Hong Liu "Research of image retrieval system framework based on ontology and content", Proc. SPIE 8349, Fourth International Conference on Machine Vision (ICMV 2011): Machine Vision, Image Processing, and Pattern Analysis, 83490M (13 January 2012);

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