政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/38537

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/38537

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政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 > Item 140.119/38537

請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/38537

題名:	基於電影拍攝手法之電影場景情緒探勘 Emotion Discovery of Movie Content Based on Film Grammar
作者:	廖家慧 Liao, Chia Hui
貢獻者:	沈錳坤 Shan, Man Kwan 廖家慧 Liao, Chia Hui
關鍵詞:	內涵式分析拍攝手法電影場景視聽覺特徵情緒 content-based analysis film grammar movie scene audiovisual features emotion affective classification
日期:	2007
上傳時間:	2010-04-09 13:17:50 (UTC+8)
摘要:	數位化的今天，電影逐漸成為人們日常生活的一部份，電影資料的內涵式分析也成為目前重要的研究主題。透過電影拍攝手法，我們知道電影視聽覺特徵與情緒之間有密不可分的關係。因此，在本研究中，我們希望利用探勘電影視聽覺特徵與情緒的關聯來達到自動判斷電影場景的情緒。首先，先由人工標記訓練場景的情緒，之後，我們對所有的場景擷取定義的六類特徵值。特徵值包括電影場景的顏色、燈光、影片速度、特寫鏡頭、聲音和字幕六類。最後，我們利用Mixed Media Graph演算法來探勘場景情緒與特徵值之間的關聯，達到自動判斷電影場景情緒的功能。實驗結果顯示，準確率最高可達到70%。 Movies play an important role in our life nowadays. How to analyze the emotional content of movies becomes one of the major issues. Based on film grammar, there are many audiovisual cues in movies helpful for detecting the emotions of scenes. In this research, we investigate the discovery of the relationship between audiovisual cues and emotions of scenes and the automatic emotion annotation of scenes is achieved. First, the training scenes are labeled with the emotions manually. Second, six classes of audiovisual features are extracted from all scenes. These classes of features consist of color, light, tempo, close-up, audio, and textual. Finally, the graph-based approach, Mixed Media Graph is modified to mine the association between audiovisual features and emotions of the scenes. The experiments show that the accuracy achieves 70%.
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Kang, “Affective Content Detection using HMMs,” Proceedings of ACM International Conference on Multimedia, Berkeley, California, U.S.A, pp. 259-262, November 2003. [14] G. Kirouac, Les émotions: Monographies de psychologie. Sillery: Presses de l’Université du Québec, 1992. [15] F. F. Kuo, M. F. Chiang, M. K. Shan, and S. Y. Lee, “Emotion-based Music Recommendation by Association Discovery from Film Music,” Proceedings of ACM International Conference on Multimedia, Singapore, pp. 507-510, November 2005. [16] Y. Li, S. H. Lee, C. H. Yeh, and C. C. J. Kuo, ”Techniques for Movie Content Analysis and Skimming,” IEEE Signal Processing Magazine, Vol. 23, No. 2, pp. 79-89, March 2006. [17] L. Lu, H. Jiang, and H. J. Zhang, "A Robust Audio Classification and Segmentation Method," Proceedings of ACM International Conference on Multimedia, Ottawa, Ontario, Canada, pp. 203-211, September 2001. [18] L. Lu, H. J. Zhang, and H. Jiang, "Content Analysis for Audio Classification and Segmentation," IEEE Transaction on Speech and Audio Processing, Vol. 10, No. 7, pp. 504-516, October 2002. [19] F. H. Mahnke, Color, Environmental and Human Response. New York: Van Nostrand Reinhold, 1996. [20] S. Moncrieff, C. Dorai, and S. Venkatesh, “Affect Computing in Film through Sound Energy Dynamics,” Proceedings of ACM International Conference on Multimedia, Ottawa, Ontario, Canada, pp. 525-527, September 2001. [21] A. Ortony, G. Clore, and A. Collins, The Cognitive Structure of Emotions. New York: Oxford University Press, 1988. [22] C. E. Osgood, G. J. Suci, and P. H. Tannenbaum, The Measurement of Meaning. Urbana, IL: University of Illinois Press, 1957. [23] J. Y. Pan, H. J. Yang, P. Duygulu, and C. Faloutsos, “Automatic Image Captioning,” Proceedings of IEEE International Conference on Multimedia and Expo (ICME ’04), Taipei, Taiwan, pp. 1987-1990, June 2004. [24] J. Y. Pan, H. J. Yang, C. Faloutsos, and P. Duygulu, “Automatic Multimedia Cross-modal Correlation Discovery,” Proceedings of ACM International Conference on Knowledge Discovery on Database (SIGKDD ‘04), Seattle, Washington, pp. 653-658, August 2004. [25] D. S. Park, J. S. Park, and J. H. Han, “Image Indexing Using Color Histogram in the CIELUV Color Space,” Proceedings of 5th Japan-Korea Workshop on Computer Vision, Korea, pp. 126-132, 1999. [26] G. Peeters, “A Large Set of Audio Features for Sound Description (Similarity and Classification),” in the CUIDADO project. Technical report, Ircam, Paris, France, April 2004. [27] Z. Rasheed, Y. Sheikh, and M. Shah, “On the Use of Computable Features for Film Classification,” IEEE Transaction on Circuits and Systems for Video Technology (CSVT), Vol. 15, No. 1, pp. 52-64, January 2005. [28] J. A. Russell and A. Mehrabian, “Evidence for a Three-Factor Theory of Emotions,” Journal of Research in Personality, Vol. 11, pp. 273-294, 1977. [29] J. Saunders, “Real-Time Discrimination of Broadcast Speech/Music,” Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ’96), Atlanta, Ga, Vol. 2, pp. 993-996, May 1996. [30] E. Scheirer and M. Slaney, “Construction and Evaluation of a Robust Multifeature Speech/Music Discriminator”, Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ’97), Munich, Germany, Vol. 2, pp. 1331-1334, April 1997. [31] R. E. Thayer, The Biopsychology of Mood and Arousal. New York: Oxford University Press, 1989. [32] H. L. Wang and L. F. Cheong, “Affective Understanding in Film,” IEEE Transactions on Circuits and Systems for Video Technology (CSVT), Vol. 16, No. 6, pp. 689-704, June 2006. [33] C. Y. Wei, N. Dimitrova, and S.F. Chang, “Color-Mood Analysis of Films on Syntactic and Psychological Models,” Proceedings of IEEE International Conference on Multimedia and Expo. (ICME ’04), Taipei, Taiwan, pp. 831-834, June 2004. [34] H. Zettl, Sight Sound Motion: Applied Media Aesthetics, 3rd ed. Belmont, CA: Wadsworth Publishing Company, 1998. [35] http://www.intel.com/technology/computing/opencv/index.htm [36] http://eqi.org/fw.htm
描述:	碩士國立政治大學資訊科學學系 94753027 96
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0094753027
資料類型:	thesis
顯示於類別:	[資訊科學系] 學位論文

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