›› 2012, Vol. 27 ›› Issue (6): 1119-1128.doi: 10.1007/s11390-012-1290-4

Special Issue: Artificial Intelligence and Pattern Recognition

• Special Section on Computational Visual Media • Previous Articles     Next Articles

Affective Image Colorization

Xiao-Hui Wang1 (王晓慧), Jia Jia1 (贾珈), Han-Yu Liao2 (廖捍宇), and Lian-Hong Cai1 (蔡莲红)   

  1. 1. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;
    2. Academy of Art & Design, Tsinghua University, Beijing 100084, China
  • Received:2012-09-05 Revised:2012-09-17 Online:2012-11-05 Published:2012-11-05
  • Supported by:

    This work is supported by the National Basic Research 973 Program of China under Grant No. 2011CB302201, the National Natural Science Foundation of China under Grant Nos. 61003094, 60931160443. This work is also funded by Tsinghua National Laboratory for Information Science and Technology (TNList) Cross-Discipline Foundation of China, and supported by the Innovation Fund of Tsinghua-Tencent Joint Laboratory of China.

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.

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