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English:Spectral Graph Skeletons for 3D Action Recognition 
Japanese: Kerola Tommi, 井上中順, 篠田浩一.  
English: Tommi Kerola, Nakamasa Inoue, Koichi Shinoda.  
Language English 
Journal/Book name
English:Proc. Asian Conference on Computer Vision (ACCV) 
Volume, Number, Page         pp. 1-16
Published date Nov. 1, 2014 
English:Springer International Publishing AG, Cham 
Conference name
English:The 12th Asian Conference on Computer Vision (ACCV 2014) 
Conference site
English:University Cultural Centre 
Official URL http://www.accv2014.org/
DOI https://doi.org/10.1007/978-3-319-16817-3_27
Abstract We present spectral graph skeletons (SGS), a novel graph-based method for action recognition from depth cameras.〓The contribution of this paper is to leverage a spectral graph wavelet transform (SGWT) for creating an overcomplete representation of an action signal lying on a 3D skeleton graph.〓The resulting SGS descriptor is efficiently computable in time linear in the action sequence length.〓We investigate the suitability of our method by experiments on three publicly available datasets, resulting in performance comparable to〓state-of-the-art action recognition approaches. Namely, our method achieves 91.4% accuracy on the challenging MSRAction3D dataset in the cross-subject setting.〓SGS also achieves 96.0% and 98.8% accuracy on the MSRActionPairs3D and UCF-Kinect datasets, respectively.〓While this study focuses on action recognition, the proposed framework can in general be applied to any time series of graphs.

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