Learning multi-label scene classification

doi:10.1016/j.patcog.2004.03.009

Learning multi-label scene classification

2004-09-01

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DOI：10.1016/j.patcog.2004.03.009

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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 semantic 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. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Keywords:

image understanding

semantic scene classification

multi-label classification

multi-label training

multi-label evaluation

image organization

cross-training

Jaccard similarity

Learning multi-label scene classification

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