IEEE Trans Image Process. 2017 Mar;26(3):1264-1274. doi: 10.1109/TIP.2017.2651375. Epub 2017 Jan 10.
Exemplar-based face sketch synthesis has been widely applied to both digital entertainment and law enforcement. In this paper, we propose a Bayesian framework for face sketch synthesis, which provides a systematic interpretation for understanding the common properties and intrinsic difference in different methods from the perspective of probabilistic graphical models. The proposed Bayesian framework consists of two parts: the neighbor selection model and the weight computation model. Within the proposed framework, we further propose a Bayesian face sketch synthesis method. The essential rationale behind the proposed Bayesian method is that we take the spatial neighboring constraint between adjacent image patches into consideration for both aforementioned models, while the state-of-the-art methods neglect the constraint either in the neighbor selection model or in the weight computation model. Extensive experiments on the Chinese University of Hong Kong face sketch database demonstrate that the proposed Bayesian method could achieve superior performance compared with the state-of-the-art methods in terms of both subjective perceptions and objective evaluations.
基于示例的人脸素描合成已广泛应用于数字娱乐和执法领域。在本文中，我们提出了一种基于贝叶斯的人脸素描合成框架，从概率图形模型的角度为理解不同方法的共同特性和内在差异提供了系统的解释。所提出的贝叶斯框架由两部分组成：邻居选择模型和权重计算模型。在提出的框架内，我们进一步提出了一种贝叶斯人脸素描合成方法。所提出的贝叶斯方法的基本原理是，我们同时考虑了上述两个模型中相邻图像块之间的空间邻接约束，而现有方法要么在邻居选择模型中，要么在权重计算模型中忽略了这种约束。在香港中文大学人脸素描数据库上的广泛实验表明，所提出的贝叶斯方法在主观感知和客观评估方面都优于现有方法。
