IEEE Trans Image Process. 2014 Aug;23(8):3397-411. doi: 10.1109/TIP.2014.2331137. Epub 2014 Jun 17.
This paper addresses the problem of segmenting lip region from frontal human face image. Supposing each pixel of the target image has an optimal local scale from the segmentation viewpoint, we treat the lip segmentation problem as a combination of observation scale selection and observed data classification. Accordingly, we propose a hierarchical multiscale Markov random field (MRF) model to represent the membership map of each input pixel to a specific segment and local-scale map simultaneously. Subsequently, lip segmentation can be formulated as an optimal problem in the maximum a posteriori (MAP)-MRF framework. Then, we present a rival-penalized iterative algorithm to implement the segmentation, which is independent of the number of predefined segments. The proposed method mainly features two aspects: 1) its performance is independent of the predefined number of segments, and 2) it takes into account the local optimal observation scale for each pixel. Finally, we conduct the experiments on four benchmark databases, i.e. AR, CVL, GTAV, and VidTIMIT. Experimental results show that the proposed method is robust to the segment number that changes with a speaker's appearance, and can enhance the segmentation accuracy by taking advantage of the local optimal observation scale information.
本文针对从正面人脸图像中分割嘴唇区域的问题。假设目标图像的每个像素都有一个从分割角度来看最佳的局部尺度，我们将嘴唇分割问题视为观察尺度选择和观测数据分类的组合。因此，我们提出了一种分层多尺度马尔可夫随机场 (MRF) 模型来同时表示每个输入像素属于特定段和局部尺度图的成员概率图。随后，嘴唇分割可以在最大后验 (MAP)-MRF 框架中表示为一个最优问题。然后，我们提出了一种竞争惩罚迭代算法来实现分割，该算法与预定义的段数无关。该方法主要有两个特点：1）其性能不依赖于预定义的段数，2）它考虑了每个像素的局部最优观察尺度。最后，我们在四个基准数据库上进行了实验，即 AR、CVL、GTAV 和 VidTIMIT。实验结果表明，该方法对随说话人外观变化的分段数具有鲁棒性，并且可以通过利用局部最优观察尺度信息来提高分割精度。
