High-level concept annotation using ontology and probabilistic inference

Authors:
Yuee Liu;Jinglan Zhang;Zhengrong Li;Dian Tjondronegoro
Affiliations:
Queensland University of Technology, Brisbane, Australia and Northwest A & F University, Yangling, China;Queensland University of Technology, Brisbane, Australia;Queensland University of Technology, Brisbane, Australia;Queensland University of Technology, Brisbane, Australia
Venue:
Proceedings of the First International Conference on Internet Multimedia Computing and Service
Year:
2009

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Abstract

Image annotation is a significant step towards semantic based image retrieval. Ontology is a popular approach for semantic representation and has been intensively studied for multimedia analysis. However, relations among concepts are seldom used to extract higher-level semantics. Moreover, the ontology inference is often crisp. This paper aims to enable sophisticated semantic querying of images, and thus contributes to 1) an ontology framework to contain both visual and contextual knowledge, and 2) a probabilistic inference approach to reason the high-level concepts based on different sources of information. The experiment on a natural scene database from LabelMe database shows encouraging results.