Abstract
Motion information is the basic element for analyzing video. It represents the change of video on the time-axis and plays an important role in describing the video content. In this paper, a robust motion-based, video retrieval system is proposed. At first, shot boundary detection is achieved by analyzing luminance information, and motion information of video is abstracted and analyzed. Then rough set theory is introduced to classify the shots into two classes, global motions and local motions. Finally, shots of these two types are respectively retrieved according to the motion types of submitted shots. Experiments show that it’s effective to distinguish shots with global motions from those with local motions in various types of video, and in this situation motion-information-based video retrieval are more accurate.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Adiv, G.: Determining Three-Dimensional Motion and Structure from Optical Flow Generated by Several Moving Objects. IEEE PAMI, 384–401 (1985)
Chapelle, O., Haffner, P., Vapnik, V.N.: Support Vector Machines for Histogram-Based Image Classification. IEEE Trans. on Neural Networks 10, 1055–1064 (1999)
Divakaran, A., Sun, H.: Descriptor for Spatial Distribution of Motion Activity for Compressed Video. In: SPIE, vol. 2972, pp. 392–398 (2000)
Dufaux, F., Konrad, J.: Efficient, Robust, and Fast Global Motion Estimation for Video Coding. IEEE Trans. on Image Process 9, 497–501 (2000)
Giunta, G., Mascia, U.: Estimation of Global Motion Parameters by Complex Linear Regression. IEEE Trans. on Image Process 8, 1652–1657 (1999)
Grzymała-Busse, J.W., Hu, M.: A comparison of several approaches to missing attribute values in data mining. In: Ziarko, W.P., Yao, Y. (eds.) RSCTC 2000. LNCS, vol. 2005, pp. 378–385. Springer, Heidelberg (2001)
Hu, X.H., Cercone, N.: Learning in Relational Databases: a Rough Set Approach. Computational Intelligence 11, 323–337 (1995)
Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China: http://cs.cqupt.edu.cn/videoretrieval
Knowledge Systems Group: Rosseta Technical Reference Manual (1999)
Lu, H.B., Zhang, T.J.: An Effective Shot Cut Detection Method. Journal of Image and Graphics, 805–810 (1999)
Ma, Y.F., Zhang, H.J.: Motion Pattern Based Video Classification and Retrieval. EURASIP JASP 2, 199–208 (2003)
Moving Picture Experts Group: http://www.chiariglione.org/mpeg/
MPEG Video Group: MPEG-2. ISO/IEC (1994)
MPEG Video Group: Overview of the MPEG-7 standard. ISO/IEC (2001)
Nguyen, S.H., Skowron, A.: Quantization of Real Value Attributes-Rough Set and Boolean Reasoning Approach. In: Proc. of the Second Joint Conference on Information Sciences, vol. 2, pp. 34–37 (1995)
Pawlak, Z.: Classification of objects by means of attributes, Research Report PAS 429, Institute of Computer Science, Polish Academy of Sciences (January 1981) ISSN 138-0648
Pawlak, Z.: Rough Sets, Research Report PAS 431, Institute of Computer Science, Polish Academy of Sciences (1981)
Pawlak, Z.: Rough Set. International Journal of Computer and Information Sciences 11, 341–356 (1982)
Pawlak, Z., Grzymała-Busse, J., Slowinski, R.: Rough Sets. Communications of the ACM 38, 89–95 (1995)
Skowron, A., Rauszer, C.: The Discernibility Matrices and Functions in Information System. Intelligent Decision Support Handbook of Applications and Advances of the Rough Sets Theory 2, 331–338 (1992)
Sudhir, G., Lee, J.C.M.: Video Annotation by Motion Interpretation Using Optical Flow Streams. Journal of Visual Communication and Image Representation 7, 354–368 (1996)
Tan, Y.P., Saur, D.D., Kulkarni, S.R., Ramadge, P.J.: Rapid Estimation of Camera Motion from Compressed Video with Application to Video Annotation. IEEE Tans. on Circuits Syst. Video Techo. 10, 133–145 (2000)
Tekalp, A.M.: Digital Video Processing. Prentice-Hall, Englewood Cliffs (1995)
Wang, G.Y., Zhao, J., An, J.J., Wu, Y.: Theoretical Study on Attribute Reduction of Rough Set Theory: in Algebra View and Information View. In: Third International Conference on Cognitive Informatics, pp. 148–155 (2004)
Wang, G.Y.: Rough Set Theory and Knowledge Acquisition. Xi’an Jiaotong University 20, 102–116 (2001)
Wang, G.Y., Yang, D.C., Yu, H.: Condition-Information-Entropy-Based Decision Table Reduction. Chinese Journal of Computers 2, 759–766 (2002)
Wang, G.Y., Liu, F., Wu, Y.: Generating Rules and Reasoning under Inconsistencies. In: Proceedings of IEEE Intl. Conf. on Industrial Electronics, Control and Instrumentation, pp. 646–649 (2000)
Yin, D.S., Wang, G.Y., Wu, Y.: A Self-learning Algorithm for Decision Tree Pre-pruning. In: Proceedings of the Third International Conference on Machine Learning and Cybernetics, pp. 2140–2145 (2004)
Yoo, K.Y., Kim, J.K.: A New Fast Local Motion Estimation Algorithm Using Global Motion. Signal Processing 68, 219–224 (1998)
Yu, T.L., Zhang, S.J.: Video Retrieval Based on the Global Motion Information. Acta Electronica Sinica 29, 1794–1798 (2001)
Zhang, H.J., Kankanhalli, Smoliar, A.: Automatic Partitioning of Video. Multimedia System, 10–28 (1993)
Wang, G.Y., Zheng, Z., Zhang, Y.: RIDAS– A Rough Set Based Intelligent Data Analysis System. In: Proceedings of the First Int. Conf. on Machine Learning and Cybernetics, pp. 646–649 (2002)
Zhong, D., Chang, S.F.: Video Object Model and Segmentation for Content-Based Video Indexing. In: Proc. ISCAS 1997, pp. 1492–1495 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yuan, Z., Wu, Y., Wang, G., Li, J. (2006). Motion-Information-Based Video Retrieval System Using Rough Pre-classification. In: Peters, J.F., Skowron, A. (eds) Transactions on Rough Sets V. Lecture Notes in Computer Science, vol 4100. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11847465_15
Download citation
DOI: https://doi.org/10.1007/11847465_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-39382-5
Online ISBN: 978-3-540-39383-2
eBook Packages: Computer ScienceComputer Science (R0)