{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:30:04Z","timestamp":1750307404652,"version":"3.41.0"},"reference-count":30,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2011,2,1]],"date-time":"2011-02-01T00:00:00Z","timestamp":1296518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2011,2]]},"abstract":"<jats:p>3D human motion capture is a form of multimedia data that is widely used in entertainment as well as medical fields (such as orthopedics, physical medicine, and rehabilitation where gait analysis is needed). These applications typically create large repositories of motion capture data and need efficient and accurate content-based retrieval techniques. 3D motion capture data is in the form of multidimensional time-series data. To reduce the dimensions of human motion data while maintaining semantically important features, we quantize human motion data by extracting spatio-temporal features through SVD and translate them onto a symbolic sequential representation through our proposed sGMMEM (semantic Gaussian Mixture Modeling with EM). In order to handle variations in motion capture data due to human body characteristics and speed of motion, we transform the semantically quantized values into a histogram representation. This representation is used as a signature for classification and similarity-based retrieval. We achieved good classification accuracies for \u201ccoarse\u201d human motion categories (such as walking 92.85%, run 91.42%, and jump 94.11%) and even for subtle categories (such as dance 89.47%, laugh 83.33%, basketball signal 85.71%, golf putting 80.00%). Experiments also demonstrated that the proposed approach outperforms earlier techniques such as the wMSV (weighted Motion Singular Vector) approach and LB_Keogh method.<\/jats:p>","DOI":"10.1145\/1925101.1925104","type":"journal-article","created":{"date-parts":[[2011,3,2]],"date-time":"2011-03-02T18:19:53Z","timestamp":1299089993000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Knowledge discovery from 3D human motion streams through semantic dimensional reduction"],"prefix":"10.1145","volume":"7","author":[{"given":"Yohan","family":"Jin","sequence":"first","affiliation":[{"name":"MySpace Inc."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Balakrishnan","family":"Prabhakaran","sequence":"additional","affiliation":[{"name":"University of Texas, Dallas, TX"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2011,3,7]]},"reference":[{"volume-title":"Proceedings of the IAPR International Workshop on Multimedia Information Analysis and Retrieval. 80--95","author":"Adjeroh D. 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In Proceedings of the International Workshop on Multi-Media Database Management Systems (IW-MMDBS)."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/1467-8659.00433"},{"volume-title":"Pattern Recognition and Machine Learning","author":"Bishop C. M.","key":"e_1_2_1_4_1","unstructured":"Bishop , C. M. 2006. Pattern Recognition and Machine Learning . Springer . CMU Motion Capture Library. CMU motion capture library homepage. http:\/\/mocap.cs.cmu.edu Bishop, C. M. 2006. Pattern Recognition and Machine Learning. Springer. CMU Motion Capture Library. CMU motion capture library homepage. http:\/\/mocap.cs.cmu.edu"},{"volume-title":"Proceedings of the 29th ACM SIGGRAPH Sketches and Applications.","author":"Cardle M.","key":"e_1_2_1_5_1","unstructured":"Cardle , M. , Vlachos , M. , Brooks , S. , Keogh , E. , and Gunopulos , D . 2003. Fast motion capture matching with replicated motion editing . In Proceedings of the 29th ACM SIGGRAPH Sketches and Applications. Cardle, M., Vlachos, M., Brooks, S., Keogh, E., and Gunopulos, D. 2003. Fast motion capture matching with replicated motion editing. In Proceedings of the 29th ACM SIGGRAPH Sketches and Applications."},{"key":"e_1_2_1_6_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum likelihood from incomplete data via the EM algorithm","volume":"39","author":"Dempster A. P.","year":"1977","unstructured":"Dempster , A. P. , Laird , N. M. , and Rubin , D. B. 1977 . Maximum likelihood from incomplete data via the EM algorithm . J. Royal Statist. Soc. B39 , 1 -- 38 . Dempster, A. P., Laird, N. M., and Rubin, D. B. 1977. Maximum likelihood from incomplete data via the EM algorithm. J. Royal Statist. Soc. B39, 1--38.","journal-title":"J. Royal Statist. Soc."},{"key":"e_1_2_1_7_1","unstructured":"Duda R. O. Hart P. E. and Stork D. G. 2001. Pattern Classification. John Wiley and Sons New York. Duda R. O. Hart P. E. and Stork D. G. 2001. Pattern Classification. John Wiley and Sons New York."},{"volume-title":"Matrix Computation","author":"Golub G. H.","key":"e_1_2_1_8_1","unstructured":"Golub , G. H. and van Loan , C. F. 1996. Matrix Computation . The Johns Hopkins University Press , Baltimore, MD . Golub, G. H. and van Loan, C. F. 1996. Matrix Computation. The Johns Hopkins University Press, Baltimore, MD."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2007.154"},{"volume-title":"Proceedings of the 6th IEEE-RAS International Conference on Humanoid Robotics (HUMANOIDS'06)","author":"Guerra-Filho G.","key":"e_1_2_1_10_1","unstructured":"Guerra-Filho , G. and Aloimonos , Y . 2006. A sensory-motor language for human activity understanding . In Proceedings of the 6th IEEE-RAS International Conference on Humanoid Robotics (HUMANOIDS'06) . 69--75. Guerra-Filho, G. and Aloimonos, Y. 2006. A sensory-motor language for human activity understanding. In Proceedings of the 6th IEEE-RAS International Conference on Humanoid Robotics (HUMANOIDS'06). 69--75."},{"volume-title":"Proceedings of the International Multimedia Modeling Conference (MMM).","author":"Jin Y.","key":"e_1_2_1_11_1","unstructured":"Jin , Y. and Prabhakaran , B . 2008. Semantic quantization of 3D human motion capture data through spatial-temporal feature extraction . In Proceedings of the International Multimedia Modeling Conference (MMM). Jin, Y. and Prabhakaran, B. 2008. Semantic quantization of 3D human motion capture data through spatial-temporal feature extraction. In Proceedings of the International Multimedia Modeling Conference (MMM)."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/83.841942"},{"volume-title":"Proceedings of the International Joint Conference on Neural Networks. 725--730","author":"Kohonen T.","key":"e_1_2_1_13_1","unstructured":"Kohonen , T. , Kangas , J. , Laaksonen , J. , and Torkkola , K . 1992. A program package for the correct application of learning vector quantization algorithms . In Proceedings of the International Joint Conference on Neural Networks. 725--730 . Kohonen, T., Kangas, J., Laaksonen, J., and Torkkola, K. 1992. A program package for the correct application of learning vector quantization algorithms. In Proceedings of the International Joint Conference on Neural Networks. 725--730."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/1015706.1015760"},{"volume-title":"Proceedings of the 30th International Conference on Very Large Databases (VLDB'04)","author":"Keogh E.","key":"e_1_2_1_15_1","unstructured":"Keogh , E. , Palpanas , T. , Zordan , V. B. , Gunopulos , D. , and Cardle , M . 2004. Indexing large human-motion databases . In Proceedings of the 30th International Conference on Very Large Databases (VLDB'04) . 780--791. Keogh, E., Palpanas, T., Zordan, V. B., Gunopulos, D., and Cardle, M. 2004. Indexing large human-motion databases. 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Multimedia Tools Appl."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/1236471.1236475"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2003.06.001"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/1066157.1066290"},{"volume-title":"Department of Computer Science","author":"Meredith M.","key":"e_1_2_1_21_1","unstructured":"Meredith , M. and Maddock , S . Motion capture file format explained. Tech. rep ., Department of Computer Science , University of Sheffield. Meredith, M. and Maddock, S. Motion capture file format explained. Tech. rep., Department of Computer Science, University of Sheffield."},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/1073204.1073247"},{"key":"e_1_2_1_23_1","unstructured":"NCBI. National center for biotechnology information. http:\/\/www.ncbi.nlm.nih.gov\/ NCBI. 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