{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T21:19:39Z","timestamp":1769635179973,"version":"3.49.0"},"reference-count":36,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,5,3]],"date-time":"2021-05-03T00:00:00Z","timestamp":1620000000000},"content-version":"vor","delay-in-days":122,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>There are complex posture changes in dance movements, which lead to the low accuracy of dance movement recognition. And none of the current motion recognition uses the dancer\u2019s attributes. The attribute feature of dancer is the important high\u2010level semantic information in the action recognition. Therefore, a dance movement recognition algorithm based on feature expression and attribute mining is designed to learn the complicated and changeable dancer movements. Firstly, the original image information is compressed by the time\u2010domain fusion module, and the information of action and attitude can be expressed completely. Then, a two\u2010way feature extraction network is designed, which extracts the details of the actions along the way and takes the sequence image as the input of the network. Then, in order to enhance the expression ability of attribute features, a multibranch spatial channel attention integration module (MBSC) based on an attention mechanism is designed to extract the features of each attribute. Finally, using the semantic inference and information transfer function of the graph convolution network, the relationship between attribute features and dancer features can be mined and deduced, and more expressive action features can be obtained; thus, high\u2010performance dance motion recognition is realized. The test and analysis results on the data set show that the algorithm can recognize the dance movement and improve the accuracy of the dance movement recognition effectively, thus realizing the movement correction function of the dancer.<\/jats:p>","DOI":"10.1155\/2021\/9935900","type":"journal-article","created":{"date-parts":[[2021,5,3]],"date-time":"2021-05-03T21:05:45Z","timestamp":1620075945000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["[Retracted] Dance Movement Recognition Based on Feature Expression and Attribute Mining"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4897-1511","authenticated-orcid":false,"given":"Xianfeng","family":"Zhai","sequence":"first","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,5,3]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"crossref","unstructured":"KuehneH. JhuangH. GarroteE.et al. 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