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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2018,5,31]]},"abstract":"<jats:p>In mainstream approaches for 3D human action recognition, depth and skeleton features are combined to improve recognition accuracy. However, this strategy results in high feature dimensions and low discrimination due to redundant feature vectors. To solve this drawback, a multi-feature selection approach for 3D human action recognition is proposed in this paper. First, three novel single-modal features are proposed to describe depth appearance, depth motion, and skeleton motion. Second, a classification entropy of random forest is used to evaluate the discrimination of the depth appearance based features. Finally, one of the three features is selected to recognize the sample according to the discrimination evaluation. Experimental results show that the proposed multi-feature selection approach significantly outperforms other approaches based on single-modal feature and feature fusion.<\/jats:p>","DOI":"10.1145\/3177757","type":"journal-article","created":{"date-parts":[[2018,5,23]],"date-time":"2018-05-23T15:08:42Z","timestamp":1527088122000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Multifeature Selection for 3D Human Action Recognition"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5978-7619","authenticated-orcid":false,"given":"Min","family":"Huang","sequence":"first","affiliation":[{"name":"Xiamen University, Xiamen, Fujian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Song-Zhi","family":"Su","sequence":"additional","affiliation":[{"name":"Xiamen University, Xiamen, Fujian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong-Bo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Huaqiao University, Xiamen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guo-Rong","family":"Cai","sequence":"additional","affiliation":[{"name":"Jimei University, Xiamen, Fujian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongying","family":"Gong","sequence":"additional","affiliation":[{"name":"Xiamen University, Xiamen, Fujian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Donglin","family":"Cao","sequence":"additional","affiliation":[{"name":"Xiamen University, Xiamen, Fujian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shao-Zi","family":"Li","sequence":"additional","affiliation":[{"name":"Xiamen University, Xiamen, Fujian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,5,22]]},"reference":[{"volume-title":"Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201912)","author":"Wang J.","key":"e_1_2_1_1_1"},{"volume-title":"European Conference on Computer Vision (ECCV\u201912)","author":"Wang J.","key":"e_1_2_1_2_1"},{"volume-title":"Stop: Space-time occupancy patterns for 3d action recognition from depth map sequences. 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