Abstract
Motion retrieval has important practical value for the reuse of motion capture data. However, it is a challenging task to represent the motion data effectively due to the complexity of the motion data structure. As graph models is an effective way to represent structured data. This paper proposes a new method for human motion retrieval based on graph model. First, a method of graph model constructing based on Maximum Range of the Distance (MRD) is proposed. The MRD is used to select the joint pairs that are deemed important for a given motion, and different motions have different graph model structures. After that, similar motions can be retrieved by matching the similarity of the attributes of graph model. In the process of motion retrieval, cosine similarity is defined to measure the similarity of graph models. The experimental results show that the method proposed in this article is better than the previous methods of motion retrieval in many ways.
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Acknowledgement
This work is supported by the National Natural Science Foundation of China (Nos. 61402164 and 61751203), Program for Changjiang Scholars and Innovative Research Team in University (No. IRT_15R07), Program for the Liaoning Distinguished Professor, by the Science and Technology Innovation Fund of Dalian (No. 2018J12GX036), and by the High-level talent innovation support project of Dalian (No. 2017RD11).
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Wu, Q., Liu, R., Zhou, D., Zhang, Q. (2019). 3D Human Motion Retrieval Based on Graph Model. In: El Rhalibi, A., Pan, Z., Jin, H., Ding, D., Navarro-Newball, A., Wang, Y. (eds) E-Learning and Games. Edutainment 2018. Lecture Notes in Computer Science(), vol 11462. Springer, Cham. https://doi.org/10.1007/978-3-030-23712-7_29
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DOI: https://doi.org/10.1007/978-3-030-23712-7_29
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