Predicting emotions in user-generated videos

Proceedings of the National Conference on Artificial Intelligence

Volumn 1, Issue , 2014, Pages 73-79

  Jiang, Yu Gang ^a   Xu, Baohan ^a   Xue, Xiangyang ^a  

^a FUDAN UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

USER-GENERATED VIDEO;

EID: 84908205564     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (29)

1. Baveye, Y.; Bettinelli, J.-N.; Dellandrea, E.; Chen, L.; and Chamaret, C. 2013. A large video data base for computational models of induced emotion. In Proc. of Humaine Association Conference on Affective Computing and Intelligent Interaction.
2. Borth, D.; Chen, T.; Ji, R.-R.; and Chang, S.-F. 2013. Sentibank: Large-scale ontology and classiers for detecting sentiment and emotions in visual content. In Proc. of ACM Multimedia.
3. Dalai, N., and Triggs, B. 2005. Histograms of oriented gradients for human detection. In Proc. of IEEE Conference on Computer Vision and Pattern Recognition.
4. Jaimes, A.; Nagamine, T.; Liu, J.; and Omura, K. 2005. Affective meeting video analysis. In Proc. of IEEE International Conference on Multimedia and Expo.
5. Jiang, Y.-G.; Wang, Y.; Feng, R.; Xue, X.; Zheng, Y.; and Yang, H. 2013. Understanding and predicting interestingness of videos. In Proc. of AAA/Conference on Artificial Intelligence.
6. Joshi, D.; Datta, R.; Fedorovskaya, E.; Luong, Q.-T.; Wang, J. Z.; Li, J.; and Luo, J. 2011. Aesthetics and emotions in images. IEEE Signal Processing Magazine 28(5):94-115.
7. Kang, H.-B. 2003. Affective content detection using HMMs. In Proc. of ACM Multimedia.
8. Lang, P. J.; Bradley, M. M.; and Cuthbert, B. N. 2008. International affective picture system (IAPS): Affective ratings of pictures and instruction manual. Technical Report A-8. University of Florida, Gainesville, FL.
9. Lazebnik, S.; Schmid, C.; and Ponce, J. 2006. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In Proc. of IEEE Conference on Computer Vision and Pattern Recognition.
10. Li, L.; Su, H.; Xing, E.; and Fei-Fei, L. 2010. Object Bank: A high-level image representation for scene classification semantic feature sparsification. In Proc. of Advances in Neural Information Processing Systems.
11. Lowe, D. 2004. Distinctive image features from scale-invariant keypoints. International Journal on Computer Vision 60:91-110.
12. Lu, X.; Suryanarayan, P.; Adams, R. B.; Li, J.; Newman, M. G.; and Wang, J. Z. 2012. On shape and the computability of emotions. In Proc. of ACM Multimedia.
13. Machajdik, J., and Hanbury, A. 2010. Affective image classication using features inspired by psychology and art theory. In Proc. of ACM Multimedia.
14. Murray, N.; Marchesotti, L.; and Perronnin, F. 2012. AVA: A large-scale database for aesthetic visual analysis. In Proc. of IEEE Conference on Computer Vision and Pattern Recognition.
15. Ojala, T.; Pietikainen, M.; and Maenpaa, T. 2002. Multireso- lution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7):971-987.
16. Oliva, A., and Torralba, A. 2001. Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision 42:145-175.
17. Plutchik, R. 1980. Emotion: Theory, Research, and Experience. Number Volume 1. Academic Press.
18. Rasheed, Z.; Sheikh, Y.; and Shah, M. 2005. On the use of computable features for film classification. IEEE Trans, on Circuits and Systems for Video Technology 15(1):52-64.
19. Ren, R.; Jose, J.; and Yin, H. 2007. Affective sports highlight detection. In Proc. of European Signal Processing Conference.
20. Schuller, B.; Steidl, S.; Batliner, A.; and et al. 2013. The INTERSPEECH 2013 computational paralinguistics challenge: Social signals, conflict, emotion, autism. In Proc. of Interspeech Conference.
21. Shechtman, E., and Irani, M. 2007. Matching local self- similarities across images and videos. In Proc. of IEEE Conference on Computer Vision and Pattern Recognition.
22. Sivic, J., and Zisserman, A. 2003. Video Google: A text retrieval approach to object matching in videos. In Proc. of IEEE International Conference on Computer Vision.
23. Stein, B. E., and Stanford, T. R. 2008. Multisensory integration: current issues from the perspective of the single neuron. Nature Reviews Neuroscience 9:255-266.
24. Teixeira, R. M. A.; Yamasaki, T.; and Aizawa, K. 2012. Determination of emotional content of video clips by low- level audiovisual features. Multimedia Tools and Applications 61(1):21-49.
25. Torresani, L.; Szummer, M.; and Fitzgibbon, A. 2010. Efficient object category recognition using classemes. In Proc. of European Conference on Computer Vision.
26. Wang, H.-L., and Cheong, L.-F. 2006. Affective understanding in film. IEEE Trans, on Circuits and Systems for Video Technology 16(6):689-704.
27. Xiao, J.; Hays, J.; Ehinger, K.; Oliva, A.; and Torralba, A. 2010. SUN database: Large-scale scene recognition from abbey to zoo. In Proc. of IEEE Conference on Computer Vision and Pattern Recognition.
28. Xu, M.; Xu, C.; He, X.; Jin, J. S.; Luo, S.; and Rui, Y. 2012. Hierarchical affective content analysis in arousal and valence dimensions. Signal Processing 93(8):2140-2150.
29. Yanulevskaya, V.; van Gemert, J. C.; Roth, K.; Herbold, A. K.; Sebe, N.; and Geusebroek, J. M. 2008. Emotional valence categorization using holistic image features. In Proc. of IEEE International Conference on Image Processing.