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...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.展开更多
基金supported by the National Basic Research 973 Program of China under Grant No.2011CB302201the National Natural Science Foundation of China under Grant Nos.61003094,60931160443+1 种基金funded by Tsinghua National Laboratory for Information Science and Technology(TNList) Cross-Discipline Foundation of Chinasupported by the Innovation Fund of Tsinghua-Tencent Joint Laboratory of China
文摘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.
