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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국지능시스템학회 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS Vol.18 No.4
- 발행연도
- 2018.12
- 수록면
- 245 - 253 (9page)
- DOI
- 10.5391/IJFIS.2018.18.4.245
이용수
초록· 키워드
오류제보하기
Emotional evaluation of video clips is the difficult task because it includes not only stationary objects as the background but also dynamic objects as the foreground. In addition, there are many video analysis problems to be solved beforehand to properly address the emotionrelated tasks. Recently, however, the convolutional neural network (CNN)-based deep learning approach, opens the possibility by solving the action recognition problem. Inspired by the CNN-based action recognition technology, this paper challenges to evaluate the emotion of video clips. In the paper, we propose a deep learning model to capture the video features and evaluate the emotion of a video clip on Thayer 2D emotion space. In the model, the pre-trained convolutional 3D neural network (C3D) generates short-term spatiotemporal features of the video, LSTM accumulates those consecutive time-varying features to characterize long-term dynamic behaviors, and multilayer perceptron (MLP) evaluates emotion of a video clip by regression on the emotion space. Due to the limited number of labeled data, the C3D is employed to extract diverse spatiotemporal from various layers by transfer learning technique. The pre-trained C3D on the Sports-1M dataset and long short term memory (LSTM) followed by the MLP for regression are trained in end-to-end manner to fine-tune the C3D, and to adjust weights of LSTM and the MLP-type emotion estimator. The proposed method achieves the concordance correlation coefficient values of 0.6024 for valence and 0.6460 for arousal, respectively. We believe this emotional evaluation of video could be easily associated with appropriate music recommendation, once the music is emotionally evaluated in the same high-level emotional space.
목차
Abstract
1. Introduction
2. Related Works
3. Proposed Method
4. Experimental Results
5. Conclusion
References
참고문헌 (21)
참고문헌 신청
[학술지(정기간행물)] - Ashkan Yazdani / 2013 / Multimedia content analysis for emotional characterization of music video clips / EURASIP Journal on Image and Video Processing 2013 (1)
[학술지(정기간행물)] - Sutjipto Arifin / 2008 / Affective Level Video Segmentation by Utilizing the Pleasure-Arousal-Dominance Information / IEEE Transactions on Multimedia 10 (7) : 1325 ~ 1341
[학술대회논문] - S. Zhao / 2011 / Video indexing and recommendation based on affective analysis of viewers / Proceedings of the 19th ACM International Conference on Multimedia : 1473 ~ 1476
[학술지(정기간행물)] - Shuiwang Ji / 2013 / 3D Convolutional Neural Networks for Human Action Recognition / IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (1) : 221 ~ 231
[인터넷자원] - H. Salehinejad / / Recent advances in recurrent neural networks
[학술지(정기간행물)] - Sutjipto Arifin / 2008 / Affective Level Video Segmentation by Utilizing the Pleasure-Arousal-Dominance Information / IEEE Transactions on Multimedia 10 (7) : 1325 ~ 1341
[학술대회논문] - S. Zhao / 2011 / Video indexing and recommendation based on affective analysis of viewers / Proceedings of the 19th ACM International Conference on Multimedia : 1473 ~ 1476
[학술지(정기간행물)] - Shuiwang Ji / 2013 / 3D Convolutional Neural Networks for Human Action Recognition / IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (1) : 221 ~ 231
[인터넷자원] - H. Salehinejad / / Recent advances in recurrent neural networks
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UCI(KEPA) : I410-ECN-0101-2019-003-000309419