Abstract
In this work, we develop a novel system for synthesizing user specified emotional affection onto arbitrary input images. To tackle the subjectivity and complexity issue of the image affection generation process, we propose a learning framework which discovers emotion-related knowledge, such as image local appearance distributions, from a set of emotion annotated images. First, emotion-specific generative models are constructed from color features of the image super-pixels within each emotion-specific scene subgroup. Then, a piece-wise linear transformation is defined for aligning the feature distribution of the target image to the statistical model constructed from the given emotion-specific scene subgroup. Finally, a framework is developed by further incorporation of a regularization term enforcing the spatial smoothness and edge preservation for the derived transformation, and the optimal solution of the objective function is sought via standard non-linear optimization. Intensive user studies demonstrate that the proposed image emotion synthesis framework can yield effective and natural effects.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bianchi-Berthouze, N.: K-dime: an affective image filtering system. TMM 10, 103–106 (2003)
Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press (2004)
Chang, Y., Saito, S., Nakajima, M.: Example-based color transformation of image and video using basic color categories. TIP 16, 329–336 (2007)
Cheng, B., Ni, B., Yan, S., Tian, Q.: Learning to photograph. In: ACM MM. pp. 291–300 (2010)
Cho, S.B.: Emotional image and musical information retrieval with interactive genetic algorithm. In: Proceedings of the IEEE. pp. 702–711 (2004)
Chua, T.S., Tang, J., Hong, R., Li, H., Luo, Z., Zheng, Y.T.: Nus-wide: A real-world web image database from national university of singapore. In: CIVR (2009)
Colombo, C., Bimbo, A.D., Pala, P.: Semantics in visual information retrieval. TMM 6, 38–53 (1999)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR. pp. 886–893 (2005)
Felzenszwalb, P., Huttenlocher, D.: Efficient graph-based image segmentation. IJCV 59, 167–181 (2004)
Guo, Y., Yu, J., Xu, X., Wang, J., Peng, Q.: Example based painting generation. CGI 7(7), 1152–1159 (2006)
Gupta, M.R., Upton, S., Bowen, J.: Simulating the effect of illumination using color transformation. SPIE CCI 111, 248–258 (2005)
Hanjalic, A.: Extracting moods from pictures and sounds: towards truly personalized tv. IEEE Signal Processing Magazine 23, 90–100 (2006)
Hayashi, T., Hagiwara, M.: Image query by impression words-the iqi system. TCE 44, 347–352 (1998)
Hong, R., Yuan, X., Xu, M., Wang, M., Yan, S., Chua, T.S.: Movie2comics: A feast of multimedia artwork. In: ACM MM. pp. 611–614 (2010)
Itten, J.: The art of color: the subjective experience and objective rationale of color. John Wiley, New York (1973)
Machajdik, J., Hanbury, A.: Affective image classification using features inspired by psychology and art theory. In: ACM MM. pp. 83–92 (2010)
Ni, B., Song, Z., Yan, S.: Web image mining towards universal age estimator. In: ACM MM. pp. 85–94 (2009)
Reinhard, E., Ashikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. CGA 21 (2001)
Stricker, M., Orengo, M.: Similarity of color images. In: SPIE. pp. 381–392 (1995)
Thompson, W.B., Shirley, P., Ferwerda, J.A.: A spatial post-processing algorithm for images of night scenes. Journal of Graphics Tools 7, 1–12 (2002)
Wang, H.L., Cheong, L.F.: Affective understanding in film. TCSVT 16, 689–704 (2006)
Wang, W.N., Yu, Y.L., Jiang, S.M.: Image retrieval by emotional semantics: A study of emotional space and feature extraction. In: IEEE International Conference on Systems, Man and Cybernetics. pp. 3534–3539 (2006)
Wu, Q., Zhou, C., Wang, C.: Content-based affective image classification and retrieval using support vector machines. Affective Computing and Intelligent Interaction (2005)
Yanulevskaya, V., van Gemert, J.C., Roth, K., Herbold, A.K., Sebe, N., Geusebroek, J.M.: Emotional valence categorization using holistic image features. In: ICIP (2008)
Zhang, X., Constable, M., He, Y.: On the transfer of painting style to photographic images through attention to colour contrast. In: Pacific-Rim Symposium on Image and Video Technology. pp. 414–421 (2010)
Lang, P.J., Bradley, M.M., Cuthbert, B.N.: International affective picture system (IAPS): Affective ratings of pictures and instruction manual. In: Technical Report A-8. University of Florida, Gainesville, FL.(2008)
Acknowledgements
This research is done for CSIDM Project No. CSIDM- 200803 partially funded by a grant from the National Research Foundation (NRF) administered by the Media Development Authority (MDA) of Singapore. This work is partially supported by Human Sixth Sense Project, Illinois@Singapore Pte Ltd.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media, LLC
About this paper
Cite this paper
Xu, M., Ni, B., Tang, J., Yan, S. (2013). Image Re-Emotionalizing. In: The Era of Interactive Media. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3501-3_1
Download citation
DOI: https://doi.org/10.1007/978-1-4614-3501-3_1
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-3500-6
Online ISBN: 978-1-4614-3501-3
eBook Packages: Computer ScienceComputer Science (R0)