A novel rough sets based video shot clustering algorithm

Information Technology Journal

Volumn 10, Issue 5, 2011, Pages 1056-1060

  Zhe, Wei ^a   Zhan Ming, Li ^a   Yan Fang, Qi ^a   Li Dong, Zhao ^a  

^a LANZHOU UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Compressed domain; DC cpeffocoemts; DCT coefficients; Rough sets; Video clustering

Indexed keywords

COMPRESSED DOMAIN; DC CPEFFOCOEMTS; DCT COEFFICIENTS; ROUGH SET; VIDEO CLUSTERING;

INFORMATION SYSTEMS; ROUGH SET THEORY;

CLUSTERING ALGORITHMS;

EID: 79955916375     PISSN: 18125638     EISSN: 18125646     Source Type: Journal    
DOI: 10.3923/itj.2011.1056.1060     Document Type: Article

References (15)

1. Antani, S., R. Kasturi and R. Jain, 2002. A survey on the use of pattern recognition methods for abstraction, indexing and retrieval of images and video. Pattern Recog., 35: 945-965. (Pubitemid 34128817)
2. Bhandarkar, S.M. and S.R. Chandrasekaran, 2004. Parallel parsing of MPEG video on a shared-memory symmetric multiprocessor. Parallel Comput, 30: 1233-1276.
3. Chen, L.H., Y.C. Lai and H.Y. Mark-Liao, 2008. Movie scene segmentation using background information. Pattern Recog., 41: 1056-1065.
4. Choi, Y., S.J. Kim and S. Lee, 2002. Hierarchical shot clustering for video summarization. Proceedings of International Conference on Computational Science, April 21-24, Spring er-Ver lag, London, UK., pp: 1100-1107.
5. De Bruyne, S., D.V. Deursen, J.D. Cock, W.D. Neve, P. Lambert and R. van de Walle, 2008. A comprssed-domain approach for shot boundary detection on H.264/AVC bit streams. Image Commun., 23: 473-489.
6. Dhillon, P.S., S. Nowozin and C.H. Lampert, 2009. Combining appearance and motion for human action classification in videos. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, June 20-25, Miami, USA., pp: 935-942.
7. Ferman, A.M. and A.M. Tekalp, 1998. Efficient filtering and clustering methods for temporal video segmentation and visual summarization. J. Visual Commun. Image Represent, 9: 336-351.
8. Kim, K., T.H. Chalidabhongse and D. Harwood, 2005. Real-time foreground-background segmentation using codebook model. Real-Time Imag., 11: 172-185. (Pubitemid 40929458)
9. Lee, M.H., H.W. Yoo and D.S. Jang, 2006. Video scene change detection using neural network: Improved ART2. Exp. Syst. Appl., 31: 13-25.
10. Lei, B. and L.Q. Xu, 2006. Real-time outdoor video surveillance with robust foreground extraction and object tracking via multi-state transition management. Pattern Recog. Lett, 27: 1816-1825. (Pubitemid 44382700)
11. Lo, C.C. and S.J. Wang, 2001. Video segmentation using a histogram-based fuzzy c-means clustering algorithm. Comput. Stand. Interfac, 23: 429-438.
12. Money, A.G. andH. Agius, 2007. Video summarisation: A conceptual framework and survey of the state of the art. J. Visual Image Represent, 19: 121-143.
13. Sze, K.W., K.M. Lam and G. Qiu, 2005. A new key frame representation for video segment retrieval. IEEE Trans. Circuits Syst. Video Technol, 15: 1148-1155. (Pubitemid 41561858)
14. Tziakos, I., A. Cavallaro and L.Q. Xu, 2008. Video event segmentation and visualisation in non-linear subspace. Pattern Recong. Lett, 30: 123-131.
15. Yi, H., D. Rajan and L.T. Chia, 2006. A motion-based scene tree for browsing and retrieval of compressed videos. Inform. Syst, 31: 638-658. (Pubitemid 44233989)