Abstract
In past several years, more and more digital multimedia data in the forms of image, video and audio have been captured and archived. This kind of newresource is exiting, yet the sheer volume of data makes any retrieval task overwhelming and its efficient usage impossible. In order to deal with the deficiency, tagging method is required so as to browse the content of multimedia data almost instantly.
In this paper, we will focus on tagging human motion data. The motion data have the following features: movements of some body parts have influence on other body parts. We call this dependency motion association rule. Thus, the task of tagging motion data is equal to the task of expressing motion by using motion association rules. Association rules consist of symbols, which uniquely represent basic patterns. We call these basic patterns primitive motions. Primitive motions are extracted from the motion data by using segmentation and clustering processes. Finally, we will discuss some experiments to discover association rules from multi-stream of the motion data.
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References
S. D. Bay: Combining nearest neighbor classifiers through multiple feature subsets. In Proc. of 15th International Conference on Machine Learning, pages 37–45, 1998.
D. J. Berndt and J. Clifford: Finding patterns in time series: A dynamic programming approach. In U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, pages 229–248, AAAI Press, 1996.
T. Darrell and A. Pentland: Space-time gestures. In Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 335–340, 1993.
D. DeMenthon, V. Kobla, and D. Doermann: Video summarization by curve simplification. In Proc. of the sixth ACM international conference on Multimedia, pages 211–217, 1998.
T. Oates and P. R. Cohen: Searching for structure in multiple stream of data. In Proc. of 13th International Conference on Machine Learning, pages 346–354, 1996.
T. Pavlidis and S. L. Horowitz: Segmentation of plane curves. IEEE Transactions on Computers, 23(8):860–870, 1974.
Ryuta Osaki, Mitsuomi Shimada and Kuniaki Uehara: Extraction of Primitive Motion for Human Motion Recognition. In Proc. of the Second International Conference on Discovery Science (DS1999), pages 351–352, 1999.
Mitsuomi Shimada and Kuniaki Uehara: Discovery of Correlation from Multi-stream of Human Motion. In Proc. of the Third International Conference on Discovery Science (DS2000), pages 290–294, 2000.
Ryuta Osaki, Mitsuomi Shimada and Kuniaki Uehara: A Motion Recognition Method by Using Primitive Motions. In Proc. of 5th IFIP 2.6 Working Conference on Visual Database Systems, pages 117–128, 2000.
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© 2002 Springer-Verlag Berlin Heidelberg
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Uehara, K., Shimada, M. (2002). Extraction of Primitive Motion and Discovery of Association Rules from Human Motion Data. In: Arikawa, S., Shinohara, A. (eds) Progress in Discovery Science. Lecture Notes in Computer Science(), vol 2281. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45884-0_24
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DOI: https://doi.org/10.1007/3-540-45884-0_24
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