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A User-Driven Ontology Guided Image Retrieval Model

A User-Driven Ontology Guided Image Retrieval Model

Lisa Fan, Botang Li
Copyright: © 2009 |Volume: 3 |Issue: 3 |Pages: 12
ISSN: 1557-3958|EISSN: 1557-3966|ISSN: 1557-3958|EISBN13: 9781616920654|EISSN: 1557-3966|DOI: 10.4018/jcini.2009070106
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MLA

Fan, Lisa, and Botang Li. "A User-Driven Ontology Guided Image Retrieval Model." IJCINI vol.3, no.3 2009: pp.61-72. http://doi.org/10.4018/jcini.2009070106

APA

Fan, L. & Li, B. (2009). A User-Driven Ontology Guided Image Retrieval Model. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 3(3), 61-72. http://doi.org/10.4018/jcini.2009070106

Chicago

Fan, Lisa, and Botang Li. "A User-Driven Ontology Guided Image Retrieval Model," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 3, no.3: 61-72. http://doi.org/10.4018/jcini.2009070106

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Abstract

The demand for image retrieval and browsing online is growing dramatically. There are hundreds of millions of images available on the current World Wide Web. For multimedia documents, the typical keyword-based retrieval methods assume that the user has a specific goal in mind by using accurate query keywords in searching a set of images. Whereas the users may face with a repository of images whose domain is less known and content is semantically complicated, or the users may only generally know what they search for. In these cases it is difficult to decide what exact keywords to use for the query. In this article, we propose a user-centered image retrieval method that is based on the current Web, keyword-based annotation structure, and combining Ontology guided knowledge representation and probabilistic ranking. A prototype of web application for image retrieval using the proposed approach has been implemented. The model provides a recommendation subsystem to support and assist the user modifying the queries and reduces the user’s cognitive load with the searching space. Experimental results show that the image retrieval recall and precision rates increased and therefore demonstrates the effectiveness of the model.

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