中文摘要：

从图像中提取出头发区域，能够为头发分析、发型趋势预测等任务提供有利的线索．但是，头发的类内模式非常复杂，并且它与其他物体类间也常因光照复杂、表观特征相似等因素而难以分离．因此，头发分割是一个非常具有挑战性的问题．为了一定程度地解决这些问题，提出了一种由粗到细的头发分割方法．首先，该方法利用最新提出的利用视点进行主动分割（active segmentation with fixation,简称ASF）的方法，粗略提取头发分割的候选范围，保证头发区域的高召回率（准确率也许较低），并由此排除大部分与头发区域难以分离的背景区域；然后，利用特定于当前图像的头发类别信息，使用图割（graphcuts，简称Gc）法在限定的范围内进行更加精细的分割．具体地，采用均值漂移（meanshift，简称MS）方法对输入图像进行区域的过分割；然后，利用贝叶斯方法选择一些可靠的、有较大概率属于头发或背景的“种子区域”，针对头发和背景的种子区域，采用支持向量机（support vector machine,简称SVM）在线学习头发和背景的分类器，并将其用于预测每个像素或区域属于头发或背景的概率；最后，将得到的概率用以GraphCuts的初始化，求解得到最终的头发分割结果．实验结果表明，所提出的头发分割方法能够超越当前提出的头发分割方法．为了验证方法的可推广性，对其进行了一定扩展，并在马、汽车、飞机这3个类别的公开数据库上作了评测，取得了较好的性能．

英文摘要：

Segmenting hair regions from human images facilitates many tasks like hair analysis and hair style trends forecast. However, hair segmentation is quite challenging due to large within-class pattern diversity and between-class confusion resulted from complex illumination and similar appearance. To solve these problems to some extent, this paper proposes a novel coarse-to-fine hair segmentation method. Firstly, the recently published ＂active segmentation with fixation （ASF）＂ is used to coarsely define a candidate region with high-recall （but possibly low-precision） of hair pixels and exclude considerable part of the backgrounds which are easily confused with hair. Then the graph cuts （GC） method is applied to the candidate regions to perform more precise segmentation, by incorporating image-specific hair information. Specifically, Bayesian method is employed to select some reliable hair and background regions （seeds） among the ones over-segmented by mean shift. SVM classifier is then learnt online from these seeds and explored to predict hair/background likelihood probability, which is used as an initialization for performing GC algorithm. Experimental results demonstrate the approach outperforms existing hair segmentation methods. To validate the generality, the paper extends the method and achieves good results on the public databases of horse, car and aeroplane classes.