Affective image adjustment with a single word
详细信息    查看全文
  • 作者:Xiaohui Wang ; Jia Jia ; Lianhong Cai
  • 关键词:Affective image adjustment ; Color theme ; Art theory
  • 刊名:The Visual Computer
  • 出版年:2013
  • 出版时间:November 2013
  • 年:2013
  • 卷:29
  • 期:11
  • 页码:1121-1133
  • 全文大小:2112KB
  • 参考文献:1. Arnheim, R.: Art and Visual Perception: A Psychology of the Creative Eye. Univ of California Press, Berkeley (1954)
    2. Chen, T., Cheng, M.M., Tan, P., Shamir, A., Hu, S.M.: Sketch2photo: internet image montage. ACM Trans. Graph. 28(5), 124:1-24:10 (2009)
    3. Chen, T., Tan, P., Ma, L.Q., Cheng, M.M., Shamir, A., Hu, S.M.: Poseshop: human image database construction and personalized content synthesis. IEEE Trans. Vis. Comput. Graph. (2012)
    4. Cheng, M.M., Zhang, G.X., Mitra, N.J., Huang, X.L., Hu, S.M.: Global contrast based salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 409-16. IEEE Press, New York (2011)
    5. Chia, A.Y.S., Zhuo, S.J., Gupta, R.K., Tai, Y.W., Cho, S.Y., Tan, P., Lin, S.: Semantic colorization with internet images. ACM Trans. Graph. 30(6), 156:1-56:8 (2011) CrossRef
    6. Cohen-Or, D., Sorkine, O., Gal, R., Leyvand, T., Xu, Y.Q.: Color harmonization. ACM Trans. Graph. 25(3), 624-30 (2006) CrossRef
    7. Darwin, C., Ekman, P., Prodger, P.: The Expression of the Emotions in Man and Animals. Oxford University Press, New York (2002)
    8. Dong, Z.D., Dong, Q.: HowNet and the Computation of Meaning. World Scientific, Singapore (2006) CrossRef
    9. Huang, Y.C., Tung, Y.C., Chen, J.C., Wang, S.W., Wu, J.L.: An adaptive edge detection based colorization algorithm and its applications. In: Proceedings of ACM International Conference on Multimedia, MULTIMEDIA-5, pp. 351-54. ACM Press, New York (2005)
    10. Huang, H., Zhang, L., Zhang, H.C.: Arcimboldo-like collage using internet images. ACM Trans. Graph. 30(6), 155:1-55:8 (2011)
    11. Itten, J.: The Art of Color: the Subjective Experience and Objective Rationale of Color. Wiley, New York (1974)
    12. Kobayashi, S.: Color Image Scale. Kodansha International, Tokyo (1991)
    13. Kobayashi, S.: Art of Color Combinations. Kodansha International, Tokyo (1995)
    14. Lawson, C., Hanson, R.: Solving Least Squares Problems, vol. 15. Society for Industrial Mathematics, Philadelphia (1995) CrossRef
    15. Lazebnik, S., Schmid, C., Ponce, J.: Affine-invariant local descriptors and neighborhood statistics for texture recognition. In: Proceedings of 9th IEEE International Conference on Computer Vision, pp. 649-55. IEEE Press, New York (2003) CrossRef
    16. Levin, A., Lischinski, D., Weiss, Y.: Colorization using optimization. ACM Trans. Graph. 23, 689-94 (2004) CrossRef
    17. Li, C., Chen, T.: Aesthetic visual quality assessment of paintings. IEEE J. Sel. Top. Signal Process. 3(2), 236-52 (2009) CrossRef
    18. Li, Y., Ju, T., Hu, S.M.: Instant propagation of sparse edits on images and videos. Comput. Graph. Forum 29(7), 2049-054 (2010) CrossRef
    19. Lischinski, D., Farbman, Z., Uyttendaele, M., Szeliski, R.: Interactive local adjustment of tonal values. ACM Trans. Graph. 25(3), 646-53 (2006) CrossRef
    20. Liu, T., Yuan, Z.J., Sun, J.D., Wang, J.D., Zheng, N.N., Tang, X.O., Shum, H.Y.: Learning to detect a salient object. IEEE Trans. Pattern Anal. Mach. Intell. 33(2), 353-67 (2011) CrossRef
    21. Machajdik, J., Hanbury, A.: Affective image classification using features inspired by psychology and art theory. In: Proceedings of the International Conference on Multimedia, pp. 83-2. ACM Press, New York (2010)
    22. Matsuda, Y.: Color Design. Asakura Shoten, Tokyo (1995) (in Japanese)
    23. Mount, D.M., Arya, S.: ANN: a library for approximate nearest neighbor searching. In: Proc. Center for Geometric Computing Second Ann. Fall Workshop Computational Geometry (1997)
    24. O’Donovan, P., Agarwala, A., Hertzmann, A.: Color compatibility from large datasets. ACM Trans. Graph. 30(4) (2011)
    25. Reinhard, E., Adhikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. IEEE Comput. Graph. Appl. 21(5), 34-1 (2001) CrossRef
    26. Shin, Y., Kim, E.Y.: Affective prediction in photographic images using probabilistic affective model. In: Proceedings of the ACM International Conference on Image and Video Retrieval, Xi’an, China, pp. 390-97 (2010) CrossRef
    27. Solli, M., Lenz, R.: Color semantics for image indexing. In: Proceedings of 5th European Conference on Colour in Graphics, Imaging, and Vision, pp. 353-58 (2010)
    28. Soottitantawat, S., Auwatanamongkol, S.: Texture classification using an invariant texture representation and a tree matching kernel. Int. J. Comput. Sci. 8 (2011)
    29. Wang, L., Giesen, J., McDonnell, K., Zolliker, P., Mueller, K.: Color design for illustrative visualization. IEEE Trans. Vis. Comput. Graph. 14(6), 1739-754 (2008) CrossRef
    30. Wang, B.Y., Yu, Y.Z., Wong, T.T., Chen, C., Xu, Y.Q.: Data-driven image color theme enhancement. ACM Trans. Graph. 29(6), 146:1-46:10 (2010)
    31. Wang, B.Y., Yu, Y.Z., Xu, Y.Q.: Example-based image color and tone style enhancement. ACM Trans. Graph. 30(4) 64:1-4:12 (2011)
    32. Welsh, T., Ashikhmin, M., Mueller, K.: Transferring color to greyscale images. ACM Trans. Graph. 21(3), 277-80 (2002) CrossRef
    33. Xu, K., Li, Y., Ju, T., Hu, S.M., Liu, T.Q.: Efficient affinity-based edit propagation using k-d tree. ACM Trans. Graph. 28(5), 118:1-18:6 (2009)
  • 作者单位:Xiaohui Wang (1)
    Jia Jia (1)
    Lianhong Cai (1)

    1. Department of Computer Science and Technology, Tsinghua University, Beijing, China
  • ISSN:1432-2315
文摘
We present a complete system that automatically adjusts image color to meet a desired emotion. It will be more convenient for users, especially for non-professional users, to adjust an image with a semantic user input, for example, to make it lovelier. The whole algorithm is fully automatic, without any user interactions, and the inputs are simply the original image and an affective word (e.g. lovely). To achieve this goal, we solve several non-trivial problems. First, in order to find the proper color themes (template of colors) to reflect the expression of the affective word, we exploit the theoretical and empirical concepts in famous art theories and build a color theme—affective word relation model allowing efficient selection of candidate themes. Furthermore, we propose a novel strategy to select the most suitable color theme among the candidates. Second, to adjust image colors, we propose the Radial Basis Functions (RBF) based interpolation method, which is more effective in many scenarios as evidenced in experiments. We also evaluate the system with comprehensive user studies and its capability is confirmed by the results.

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700