.png){kind=icon}
.png){kind=icon}
Return
Skin detection using pairwise models
.png){kind=icon}
PRE
AI
DOI：10.1016/j.imavis.2005.06.010.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We consider a sequence of three models for skin detection built from a large collection of labeled images. Each model is a maximum entropy model with respect to constraints concerning marginal distributions. Our models are nested. The first model, called the baseline model is well known from practitioners. Pixels are considered independent. Performance, measured by the ROC curve on the Compaq Database is impressive for such a simple model. However, single image examination reveals very irregular results. The second model is a Hidden Markov model, which includes constraints that force smoothness of the solution. The ROC curve obtained shows better performance than the baseline model. Finally, color gradient is included. Thanks to, Bethe tree approximation, we obtain a simple analytical expression for the coefficients of the associated maximum entropy model. Performance, compared with previous model is once more improved. (C) 2005 Elsevier Ltd All rights reserved.
Keywords:
maximum entropy models
skin detection
Markov random field
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.2
Papers:
4.0K
Citations:
6.7K
Organization
No organization information available