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Human motion retrieval using topic model
Author(s) -
Zhu Mingyang,
Sun Huaijiang,
Lan Rongyi,
Li Bin
Publication year - 2011
Publication title -
computer animation and virtual worlds
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.225
H-Index - 49
eISSN - 1546-427X
pISSN - 1546-4261
DOI - 10.1002/cav.432
Subject(s) - computer science , human motion , motion (physics) , artificial intelligence , motion capture , animation , representation (politics) , search engine indexing , process (computing) , latent dirichlet allocation , redundancy (engineering) , information retrieval , computer vision , topic model , computer graphics (images) , politics , political science , law , operating system
Content‐based human motion retrieval is important for animators with the development of motion editing and synthesis, which need to search similar motions in large databases. Obtaining text‐based representation from quantization of mocap data turned out to be efficient. It becomes a fundamental step of many researches in human motion analysis. Geometric features are one of these techniques, which involve much prior knowledge and reduce data redundancy of numerical data. We describe geometric features as basic unit to define human motions (also called mo‐words) and view a human motion as a generative process. Therefore, we obtain topic motions, which possess more semantic information using latent Dirichlet allocation by learning from massive training examples in order to understand motions better. We combine probabilistic model with human motion retrieval and come up with a new representation of human motions and a new retrieval framework. Our experiments demonstrate its advantages, both for understanding motions and retrieval. Copyright © 2011 John Wiley & Sons, Ltd.