An Image Retrieval Model Combining Ontology and Probabilistic Ranking

An Image Retrieval Model Combining Ontology and Probabilistic Ranking

Lisa Fan (University of Regina, Canada) and Botang Li (University of Regina, Canada)
DOI: 10.4018/978-1-61350-126-9.ch004
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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 an exact goal in mind in searching a set of images whereas users normally do not know what they want, or the user faces a repository of images whose domain is less known and content is semantically complicated. In these cases it is difficult to decide what keywords to use for the query. In this chapter, we propose a user-centered image retrieval method based on the current Web, keyword-based annotation structure, and combining ontology guided knowledge representation and probabilistic ranking. A 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 reducing the user’s cognitive load with the searching space. Experimental results show that the image retrieval recall and precision rates are increased and therefore demonstrate the effectiveness of the model.
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Image Retrieval is a large and active research area of computer and information science. A summary review of the literature shows an exceptionally active community of researchers in this area. Smeulders et al. reviewed more than 200 research papers prior to 2000 (Smeulders, Worring, Santini, Gupta & Jain, 2000). Rui et al. have summarized more than 100 research papers (Rui, Huang & Chang, 1999). Recently, Datta et al. have surveyed about 300 papers, mostly published between 2000 and 2007 (Datta, Joshi, Li & Wang, 2008).

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