Refereed full papers (journals, book chapters, international conferences)

2004

@article{BLS2004,
	vgclass =	{refpap},
	author =	{Matthew R. Boutell and Jiebo Luo and Xipeng Shen and
	Christopher M. Brown},
	title =	{Learning multi-label scene classification},
	journal =	{Pattern Recognition},
	year =	{2004},
	note =	{(in press)},
	url =	{http://dx.doi.org/10.1016/j.patcog.2004.03.009},
	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 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.},
}