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Proceedings Paper

Ontology-based image classification using neural networks
Author(s): Casey Breen; Latifur Khan; Arun Kumar; Lei Wang
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Paper Abstract

Technology in the field of digital media generates huge amounts of non-textual information that proves to be very difficult to index. The key problem in achieving efficient and user-friendly retrieval in the domain of multimedia images is the development of a search mechanism to guarantee delivery of minimal irrelevant information (high precision) while insuring that relevant information is not overlooked (high recall). To provide more accurate search results we propose a system that examines the relationships among objects in images to help achieve a more detailed understanding of the content and meaning of individual images. We solve the problem of creating a meaning based index structure through the design and implementation of a concept-based model using domain dependent ontologies. The system converts objects to their meaning by identifying appropriate concepts that both describe and identify images. The system poses the ability to automatically select concepts using a disambiguation algorithm that prune irrelevant concepts and allows relevant ones to associate with images. The system uses a neural network to successfully identify objects present in images. Once identified by the neural network, the objects are fed into the domain-dependent ontologies for high precision classification of the image based on its contents.

Paper Details

Date Published: 1 July 2002
PDF: 11 pages
Proc. SPIE 4862, Internet Multimedia Management Systems III, (1 July 2002); doi: 10.1117/12.473036
Show Author Affiliations
Casey Breen, Univ. of Texas at Dallas (United States)
Latifur Khan, Univ. of Texas at Dallas (United States)
Arun Kumar, Univ. of Texas at Dallas (United States)
Lei Wang, Univ. of Texas at Dallas (United States)

Published in SPIE Proceedings Vol. 4862:
Internet Multimedia Management Systems III
John R. Smith; Sethuraman Panchanathan; Tong Zhang, Editor(s)

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