Authors:
Rudra P. K. Poudel
1
;
Hammadi Nait-Charif
1
;
Jian J. Zhang
1
and
David Liu
2
Affiliations:
1
Bournemouth University, United Kingdom
;
2
Siemens Corporate Research, United States
Keyword(s):
Skin-color Detection, Region-based, Superpixels, Bayes Classifier, Conditional Random Field.
Related
Ontology
Subjects/Areas/Topics:
Color and Texture Analyses
;
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Segmentation and Grouping
Abstract:
Skin color provides a powerful cue for complex computer vision applications. Although skin color detection has been an active research area for decades, the mainstream technology is based on the individual pixels. This paper presents a new region-based technique for skin color detection which outperforms the current state-of-the-art pixel-based skin color detection method on the popular Compaq dataset (Jones and Rehg, 2002). Color and spatial distance based clustering technique is used to extract the regions from the images, also known as superpixels. In the first step, our technique uses the state-of-the-art non-parametric pixel-based skin color classifier (Jones and Rehg, 2002) which we call the basic skin color classifier. The pixel-based skin color evidence is then aggregated to classify the superpixels. Finally, the Conditional Random Field (CRF) is applied to further improve the results. As CRF operates over superpixels, the computational overhead is minimal. Our technique achi
eves 91.17% true positive rate with 13.12% false negative rate on the Compaq dataset tested over approximately 14,000 web images.
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