Abstract
A novel motion retrieval method which combines semantic analysis with graph model is proposed. The method includes 2 main stages: (1) in stage of learning, firstly, we can get the Motion Semantic Dictionary (MSD) and the Motion Code Table (MCT) by clustering and handmade based on motion training data learning. Next, the MSD and the MCT are used to calculate system parameters, and the Hidden Markov Model (HMM) is built. For each motion in testing data, aligned cluster analysis (ACA) is used to get key frames, and semantic code is got based on HMM inference. All semantic codes of testing data are combined to construct the Semantic Code Book (SCB). (2) In stage of motion retrieval, according to the above steps, query motion code is got, and the query motion is recognized based on motion code matching. Our method has lesser time and cost than existing algorithms. The experimental results show that the proposed method is effectiveness.
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This work is partly supported by the National Science Foundation of China (Nos. 60972095, 61271362).
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Communicated by V. Loia.
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Xiao, Q., Song, R. Motion retrieval based on Motion Semantic Dictionary and HMM inference. Soft Comput 21, 255–265 (2017). https://doi.org/10.1007/s00500-016-2059-4
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DOI: https://doi.org/10.1007/s00500-016-2059-4