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

2003

@article{KLH2003,
	vgclass =	{refpap},
	author =	{Sanjiv Kumar and Alexander C. Loui and Martial Hebert},
	title =	{An observation-constrained generative approach for
	probabilistic classification of image regions},
	journal =	{Image and Vision Computing},
	volume =	{21},
	number =	{1},
	pages =	{87--97},
	month =	{January},
	year =	{2003},
	url =	{http://dx.doi.org/10.1016/S0262-8856(02)00125-7},
	abstract =	{In this paper, we propose a probabilistic region
	classification scheme for natural scene images.  In conventional
	generative methods, a generative model is learnt for each class using
	all the available training data belonging to that class. However, if an
	input image has been generated from only a subset of the model support,
	use of the full model to assign generative probabilities can produce
	serious artifacts in the probability assignments. This problem arises
	mainly when the different classes have multimodal distributions with
	considerable overlap in the feature space. We propose an approach to
	constrain the class generative probability of a set of newly observed
	data by exploiting the distribution of the new data itself and using
	linear weighted mixing. A Kullback-Leibler Divergence-based fast model
	selection procedure is also proposed for learning mixture models in a
	low dimensional feature space. The preliminary results on the natural
	scene images support the effectiveness of the proposed approach.},
}