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2001
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Category-based image retrieval

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Category-based image retrieval
This work presents a novel approach to content-based image retrieval in categorical multimedia databases. The images are indexed using a combination of text and content descriptors. The categories are viewed as semantic clusters of images and are used to confine the search space. Keywords are used to identify candidate categories. Content-based retrieval is performed in these categories using multiple image features. Relevance feedback is used to learn the user's intent--query specification and featureg--with minimal user-interface abstraction. The method is applied to a large number of images collected from a popular categorical structure on the World Wide Web. Results show that efficient and accurate performance is achievable by exploiting the semantic classification represented by the categories. The relevance feedback loop allows the content descriptor weightings to be determined without exposing the calculations to the user.
Shawn D. Newsam, Baris Sumengen, B. S. Manjunath
Added 25 Oct 2009
Updated 27 Oct 2009
Type Conference
Year 2001
Where ICIP
Authors Shawn D. Newsam, Baris Sumengen, B. S. Manjunath
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