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

2007

@article{LXF2007,
	vgclass =	{refpap},
	author =	{Bin Li and Xiangyang Xue and Jianping Fa},
	title =	{A robust incremental learning framework for accurate skin region segmentation in color images},
	journal =	{Pattern Recognition},
	year =	{2007},
	note =	{(in press)},
	url =	{http://dx.doi.org/10.1016/j.patcog.2007.04.018},
	abstract =	{In this paper, we propose a robust incremental learning
	framework for accurate skin region segmentation in real-life images.
	The proposed framework is able to automatically learn the skin color
	information from each test image in real-time and generate the Specific
	Skin Model (SSM) for that image. Consequently, the SSM can adapt to a
	certain image, in which the skin colors may vary from one region to
	another due to illumination conditions and inherent skin colors. The
	proposed framework consists of multiple iterations to learn the SSM,
	and each iteration comprises two major steps: 1) collecting new skin
	samples by region growing; 2) up dating the skin model incrementally
	with the available skin samples. After the skin model converges (i.e.,
	becomes the SSM), a post-processing can be further performed to fill up
	the interstices on the skin map. We performed a set of experiments on
	a large-scale real-life image database and our method observably
	outperformed the well-known Bayesian Histogram. The experimental
	results confirm that the SSM is more robust than static skin models.},
}