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Image Content Modeling for Meaning-based Retrieval  

나연묵 (단국대학교 전기전자컴퓨터공학부)
Publication Information
Journal of KIISE:Databases / v.30, no.2, 2003 , pp. 145-156 More about this Journal
Abstract
Most of the content-based image retrieval systems focuses on similarity-based retrieval of natural picture images by utilizing color. shape, and texture features. For the neuroscience image databases, we found that retrieving similar images based on global average features is meaningless to pathological researchers. To realize the practical content-based retrieval on images in neuroscience databases, it is essential to represent internal contents or semantics of images in detail. In this paper, we present how to represent image contents and their related concepts to support more useful retrieval on such images. We also describe the operational semantics to support these advanced retrievals by using object-oriented message path expressions. Our schemes are flexible and extensible, enabling users to incrementally add more semantics on image contents for more enhanced content searching.
Keywords
Image Databases; Image Content Modeling; Meaning-based Retrieval;
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