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

2004

@article{SSL2004,
	vgclass =	{refpap},
	author =	{Navid Serrano and Andreas E. Savakis and Jiebo Luo},
	title =	{Improved scene classification using efficient low-level
	features and semantic cues},
	journal =	{Pattern Recognition},
	year =	{2004},
	note =	{(in press)},
	url =	{http://dx.doi.org/10.1016/j.patcog.2004.03.003},
	abstract =	{Prior research in scene classification has focused on
	mapping a set of classic low-level vision features to semantically
	meaningful categories using a classifier engine. In this paper, we
	propose improving the established paradigm by using a simplified
	low-level feature set to predict multiple semantic scene attributes
	that are integrated probabilistically to obtain a final indoor/outdoor
	scene classification. An initial indoor/outdoor prediction is obtained
	by classifying computationally efficient, low-dimensional color and
	wavelet texture features using support vector machines. Similar
	low-level features can also be used to explicitly predict the presence
	of semantic features including grass and sky. The semantic scene
	attributes are then integrated using a Bayesian network designed for
	improved indoor/outdoor scene classification.},
}