ABSTRACT
According to the concepts of Large-Scale Concept Ontology for Multimedia (LSCOM) and requirement of the 4th task in the 2006 TRECVID, i.e., rushes exploitation, the "interview" concept is an important semantic concept for rushes content analysis. The paper presents the shot-level "interview" concept detection method. Face detection and audio classification are implemented to detect "face" and "speech" concepts for each shot. By integrating audiovisual information, "interview" concept is finally detected. The utilization of the method will definitely benefit the video edit. Large-scale experimental results strongly demonstrate the accuracy and effectiveness of the proposed method.
- Guidelines for the TRECVID 2006 Evaluation: http://www-nlpir.nist.gov/projects/tv2006Google Scholar
- Bradley P. Allen, Valery A. Petrushin, Searching for Relevant Video Shots in BBC Rushes Using Semantic Web Techniques, In Proc. TRECVID Workshop, 2005. http://www-nlpir.nist.gov/projects/tvpubs/tv.pubs.org.htmlGoogle Scholar
- Jin-Hau Kuo, Jen-Bin Kuo, A hierarchical and multi-modal based algorithm for lead detection and news program narrative parsing, Proc. of 19th International Conference on Advanced Information Networking and Applications, 2005, vol. 2, pp: 511--514 Google ScholarDigital Library
- Albiol, A., Torres, L., Delp, E. J., The indexing of persons in news sequences using audio-visual data, Proc. of ICASSP '03, vol. 3, pp: III- 137--40Google Scholar
- LSCOM Lexicon Definitions and Annotations, http://www.ee.columbia.edu/ln/dvmm/lscom/.Google Scholar
- Naphade, M., Smith, J. R., et al, Large-scale concept ontology for multimedia, IEEE MultiMedia, vol. 13, no. 3, pp. 86--91, 2006. Google ScholarDigital Library
- Sheng Tang, Yong-Dong Zhang, Jin-Tao Li et al. TRECVID 2006 Rushes Exploitation By CAS MCG. In Proc. TRECVID Workshop, 2006. http://www-nlpir.nist.gov/projects/tvpubs/tv.pubs.org.htmlGoogle Scholar
- Bai Liang, Hu Yaali, Lao Songyang, et al. Feature analysis and extraction for audio automatic classification. Proc. of IEEE International Conference on System, Man and Cybernetics, 2005.Google ScholarCross Ref
- Wan, M. Campbell, "Support vector machines for speaker verification and identification," Proc. of the IEEE Signal Processing Society Workshop on Neural Networks, 2000.Google Scholar
- Zhai Y., Rasheed Z., "Semantic classification of movie scenes using finite state machines", IEE Proc of Vision, Image and Signal Processing, vol. 152, pp. 896--901, 2005.Google ScholarCross Ref
- Zhao Ming, Chen Chun, Li S Z, et al. Subspace analysis and optimization for AAM based face alignment {A}.In Proc. of Sixth IEEE International Conference on Automatic Face and Gesture Recognition {C}. Seoul, South Korea, 2004. 290--295. Google ScholarDigital Library
Index Terms
- Multi-modal interview concept detection for rushes exploitation
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