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View all- Mezaris VPapadopoulos G(2009)Semantic Video Analysis and UnderstandingEncyclopedia of Information Science and Technology, Second Edition10.4018/978-1-60566-026-4.ch543(3419-3425)Online publication date: 2009
A learning approach to semantic image analysis
Article No.: 29, Pages 1 - 6
Abstract
In this paper, a learning approach coupling Support Vector Machines (SVMs) and a Genetic Algorithm (GA) is presented for knowledge-assisted semantic image analysis in specific domains. Explicitly defined domain knowledge under the proposed approach includes objects of the domain of interest and their spatial relations. SVMs are employed using low-level features to extract implicit information for each object of interest via training in order to provide an initial annotation of the image regions based solely on visual features. To account for the inherent visual information ambiguity, fuzzy spatial relations along with the previously computed initial annotations are supplied to a genetic algorithm, which decides on the globally most plausible annotation. Experiments with images of the beach vacation domain demonstrate the performance of the proposed approach.
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September 2006
281 pages
ISBN:1595935177
DOI:10.1145/1374296
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Published: 18 September 2006
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View all- Mezaris VPapadopoulos G(2009)Semantic Video Analysis and UnderstandingEncyclopedia of Information Science and Technology, Second Edition10.4018/978-1-60566-026-4.ch543(3419-3425)Online publication date: 2009
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