On the application of Bayes networks to semantic understanding of consumer photographs

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Title: On the application of Bayes networks to semantic understanding of consumer photographs
Author: Luo, Jiebo; Savakis, Andreas; Etz, Stephen; Singhal, Amit
Abstract: Belief networks, such as Bayes nets, have emerged as an effective knowledge representation and inference engine in artificial intelligence and expert systems research. Their effectiveness is due to the ability to explicitly integrate domain knowledge in the network structure and to reduce a joint probability distribution to conditionally independence relationships. Current research in content-based image processing and analysis is largely limited to low-level feature extraction and classification. The ability to extract both low-level and semantic features and perform knowledge integration of different types of features would be very useful. We present a general knowledge integration framework that incorporates Bayes networks and has been used in two applications involving semantic understanding of consumer photographs. The first application aims at detecting main photographic subjects in an image and the second aims at selecting the most appealing image in an event. With these diverse examples, we demonstrate that effective inference engines can be built according to specific domain knowledge and available training data to solve inherently uncertain vision problems.
Description: "On the application of Bayes networks to semantic understanding of consumer photographs," Proceedings of the 2000 International Conference on Image Processing, vol. 3. The Institute of Electrical and Electronics Engineers. Held in Vancouver, Canada: 10-13 September 2000. ©2000 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Record URI: http://hdl.handle.net/1850/4079
Date: 2000-09-10

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