中文摘要：

灰阶的图象的 Colorization 很长时间吸引了许多注意。图象颜色的一个重要角色是情感的 conveyer (通过颜色主题) 。有一个不希望得到的颜色主题的 colorization 是不太有用的，甚至它是语义上正确的。然而，这很少被考虑了。尊重语义和情感的自动 colorization 无疑是挑战。在这份报纸，我们为感情方面的图象 colorization 建议一个完全的系统。我们仅仅需要用户与文本标签和一个感情方面的词一起帮助对象分割。首先，与另外的对象字符一起的文本标签联合被用来过滤因特网图象给每个对象一套语义上正确的引用图象。第二，我们基于艺术理论根据感情方面的词选择一套颜色主题。与这些主题，一个通用算法被用来为每个目标选择最好的参考书，平衡各种各样的要求。最后，我们为 colorization 建议一条混合质地合成途径。就我们的知识而言，是第一个系统能高效地由感情上可控制的时尚语义上加色一幅灰阶的图象。我们的实验显示出我们的系统的有效性，特别利益与以前的 Markov 随机的地(MRF ) 相比基于方法。

英文摘要：

Colorization of gray-scale images has attracted many attentions for a long time. An important role of image color is the conveyer of emotions （through color themes）. The colorization with an undesired color theme is less useful, even it is semantically correct. However this has been rarely considered. Automatic colorization respecting both the semantics and the emotions is undoubtedly a challenge. In this paper~ we propose a complete system for affective image colorization. We only need the user to assist object segmentation along with text labels and an affective word. First, the text labels along with other object characters are jointly used to filter the internet images to give each object a set of semantically correct reference images. Second, we select a set of color themes according to the affective word based on art theories. With these themes, a generic algorithm is used to select the best reference for each object, balancing various requirements. Finally, we propose a hybrid texture synthesis approach for colorization. To the best of our knowledge, it is the first system which is able to efficiently colorize a gray-scale image semantically by an emotionally controllable fashion. Our experiments show the effectiveness of our system, especially the benefit compared with the previous Markov random field （MRF） based method.