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Proceedings Paper

Multilabel machine learning and its application to semantic scene classification
Author(s): Xipeng Shen; Matthew Boutell; Jiebo Luo; Christopher Brown
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Paper Abstract

In classic pattern recognition problems, classes are mutually exclusive by definition. Classification errors occur when the classes overlap in the feature space. We examine a different situation, occurring when the classes are, by definition, not mutually exclusive. Such problems arise in scene and document classification and in medical diagnosis. We present a framework to handle such problems and apply it to the problem of semantic scene classification, where a natural scene may contain multiple objects such that the scene can be described by multiple class labels (e.g., a field scene with a mountain in the background). Such a problem poses challenges to the classic pattern recognition paradigm and demands a different treatment. We discuss approaches for training and testing in this scenario and introduce new metrics for evaluating individual examples, class recall and precision, and overall accuracy. Experiments show that our methods are suitable for scene classification; furthermore, our work appears to generalize to other classification problems of the same nature.

Paper Details

Date Published: 18 December 2003
PDF: 12 pages
Proc. SPIE 5307, Storage and Retrieval Methods and Applications for Multimedia 2004, (18 December 2003); doi: 10.1117/12.523428
Show Author Affiliations
Xipeng Shen, Univ. of Rochester (United States)
Matthew Boutell, Univ. of Rochester (United States)
Jiebo Luo, Eastman Kodak Co. (United States)
Christopher Brown, Univ. of Rochester (United States)

Published in SPIE Proceedings Vol. 5307:
Storage and Retrieval Methods and Applications for Multimedia 2004
Minerva M. Yeung; Rainer W. Lienhart; Chung-Sheng Li, Editor(s)

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