On shape and the computability of emotions

MM 2012 - Proceedings of the 20th ACM International Conference on Multimedia

Volumn , Issue , 2012, Pages 229-238

  Lu, Xin ^a   Suryanarayan, Poonam ^a   Adams, Reginald B ^a   Li, Jia ^a,b   Newman, Michelle G ^a   Wang, James Z ^a,b  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

^b NATIONAL SCIENCE FOUNDATION   (United States)

Author keywords

human emotion; psychology; shape features

Indexed keywords

EMOTION RECOGNITION;

EMOTIONAL RESPONSE; HUMAN BEING; HUMAN EMOTION; NATURAL IMAGES; PICTURE SYSTEM; PSYCHOLOGY; SHAPE FEATURES; STATE OF THE ART; VISUAL ARTS; VISUAL PSYCHOLOGIES;

FORECASTING;

EID: 84871390800     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2393347.2393384     Document Type: Conference Paper

References (28)

1. P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik. Contour detection and hierarchical image segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence, 33(5):898-916, 2011.
2. S. Arifin and P. Y. K. Cheung. A computation method for video segmentation utilizing the pleasure-arousal-dominance emotional information. In ACM MM, pages 68-77, 2007.
3. R. Arnheim. Art and visual perception: A psychology of the creative eye. 1974.
4. J. Aronoff. How we recognize angry and happy emotion in people, places, and things. Cross-Cultural Research, 40(1):83-105, 2006.
5. M. Bar and M. Neta. Humans prefer curved visual objects. Psychological Science, 17(8):645-648, 2006.
6. M. M. Bradley and P. J. Lang. The international affective picture system(IAPS) in the study of emotion and attention. In Handbook of Emotion Elicitation and Assessment, pages 29-46, 2007.
7. S. Brandt, J. Laaksonen, and E. Oja. Statistical shape features in content-based image retrieval. In ICPR, pages 1062-1065, 2000.
8. A. Chia, D. Rajan, M. Leung, and S. Rahardja. Object recognition by discriminative combinations of line segments, ellipses and appearance features. IEEE Trans. on Pattern Analysis and Machine Intelligence, 34(9):1758-1772, 2011.
9. G. Csurka, S. Skaff, L. Marchesotti, and C. Saunders. Building look & feel concept models from color combinations. The Visual Computer, 27(12):1039-1053, 2011.
10. R. Datta, D. Joshi, J. Li, and J. Z. Wang. Studying aesthetics in photographic images using a computational approach. In ECCV, pages 288-301, 2006.
11. R. Datta, J. Li, and J. Z. Wang. Algorithmic inferencing of aesthetics and emotion in natural image: An exposition. In ICIP, pages 105-108, 2008.
12. A. Hanjalic and L. Q. Xu. Affective video content representation and modeling. IEEE Trans. on Multimedia, 7(1):143-154, 2005.
13. D. Joshi, R. Datta, E. Fedorovskaya, Q. T. Luong, J. Z. Wang, J. Li, and J. Luo. Aesthetics and emotions in images. IEEE Signal Processing Magazine, 28(5):94-115, 2011.
14. P. J. Lang, M. M. Bradley, and B. N. Cuthbert. Emotion, motivation, and anxiety: Brain mechanisms and psychophysiology. Biological Psychiatry, 44(12):1248-1263, 1998.
15. P. J. Lang, M. M. Bradley, and B. N. Cuthbert. International affective picture system: Affective ratings of pictures and instruction manual. In Technical Report A-8, University of Florida, Gainesville, FL, 2008.
16. M. K. Leung and Y.-H. Yang. Dynamic two-strip algorithm in curve fitting. Pattern Recognition, 23(1-2):69-79, 1990.
17. K. A. Lindquist, T. D. Wager, H. Kober, E. Bliss-Moreau, and L. F. Barrett. The brain basis of emotion: A meta-analytic review. Behavioral and Brain Sciences, 173(4):1-86, 2011.
18. J. Machajdik and A. Hanbury. Affective image classification using features inspired by psychology and art theory. In ACM MM, pages 83-92, 2010.
19. J. Mikel, B. L. Fredrickson, G. R. Larkin, C. M. Lindberg, S. J. Maglio, and P. A. Reuter-Lorenz. Emotional category data on images from the international affective picture system. Behavior Research Methods, 37(4):626-630, 2005.
20. Y. Mingqiang, K. Kidiyo, and R. Joseph. A survey of shape feature extraction techniques. Pattern Recognition, pages 43-90, 2008.
21. R. Reber, N. Schwarz, and P. Winkielman. Processing uency and aesthetic pleasure: Is beauty in the perceiver's processing experience? Personality and Social Psychology Review, 8(4):364-382, 2004.
22. H. R. Schiffman. Sense and Perception: An Integrated Approach. 1990.
23. T. Shibata and T. Kato. Kansei image retrieval system for street landscape-discrimination and graphical parameters based on correlation of two image systems. In International Conference on Systems, Man, and Cybernetics, pages 274-252, 2006.
24. M. Solli and R. Lenz. Color based bags-of-emotions. LNCS, 5702:573-580, 2009.
25. H. L. Wang and L. F. Cheong. Affective understanding in film. IEEE Trans. on Circuits and Systems for Video Technology, 16(6):689-704, 2006.
26. V. Yanulevskaya, J. C. Van Gemert, K. Roth, A. K. Herbold, N. Sebe, and J. M. Geusebroek. Emotional valence categorization using holistic image features. In ICIP, pages 101-104, 2008.
27. H. Zhang, E. Augilius, T. Honkela, J. Laaksonen, H. Gamper, and H. Alene. Analyzing emotional semantics of abstract art using low-level image features. In Advances in Intelligent Data Analysis, pages 413-423, 2011.
28. S. L. Zhang, Q. Tian, Q. M. Huang, W. Gao, and S. P. Li. Utilizing affective analysis for efficient movie browsing. In ICIP, pages 1853-1856, 2009.