基于Real Adaboost的肤色分割方法

提出了一种基于Real AdaBoost算法构造的肤色置信度分类器及动态阈值相结合的肤色分割方法。根据肤色在YCrCb色度空间的聚类性，通过大量肤色和非肤色样本用Real AdaBoost训练一族查找表(LUT)型圆形弱分类器，组成一个能输出连续置信度的强分类器，利用肤色强分类器计算图像中像素的肤色相似度，最后用大津法确定阈值对肤色相似图进行二值分割。实验表明，该方法能较好地描述肤色分布，误检率低,鲁棒性好。

Skin segmentation based on Real AdaBoost algorithm

This paper proposed a method for skin segmentation based on the similarity of skin-tone given by strengthened classifier which constructed via Real AdaBoost algorithm and dynamic threshold.Based on the clustering property of skin-tone distribution in YCrCb chrominance space, a set of weak classifiers in Look-Up-Table (LUT) type using circle-like features was trained via Real AdaBoost to form a strengthened classifier. Firstly, gray scale images indicated the skin-tone similarity of the pixels was created by processing the images with the strengthened classifier. Then skin segmentation was implemented according to the dynamic threshold selected through Da-Jing method. The experimental results show that the strengthened classifier has an outstanding ability for describing the distribution of skin-tone color in the YCrCb space. The method is robust, and efficient.