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The high cost of marking data will greatly reduce the effect of the supervised learning model transferring to other scene domains. Therefore, unsupervised learning of person re-identification becomes more attractive in the real world. In addition, due to changes in camera angle, illumination and posture, the extracted person image representation is generally different in the non-cross camera view, but the existing algorithm ignores the difference among cross camera images under camera parameters and environments. In order to overcome the above problems, we propose unsupervised person re-identification metric learning method. The model learns a shared space to reduce the discrepancy under different cameras. The graph convolution network is further employed to cluster the cross-view image features extracted from the shared space. Our model improves the scalability of pedestrian re-identification in practical application scenarios. Extensive experiments on four large-scale person re-identification public datasets have been conducted to demonstrate the effectiveness of the proposed model.<\/jats:p>","DOI":"10.3233\/jifs-200435","type":"journal-article","created":{"date-parts":[[2020,7,31]],"date-time":"2020-07-31T12:54:59Z","timestamp":1596200099000},"page":"4453-4462","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Cross-view pedestrian clustering via graph convolution network for unsupervised person re-identification"],"prefix":"10.1177","volume":"39","author":[{"given":"Yuanyuan","family":"Wang","sequence":"first","affiliation":[{"name":"College of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, China"}]},{"given":"Xiang","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, China"}]},{"given":"Mingxin","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Huaiyin Institute of Technology, Huaian, China"}]},{"given":"Haiyan","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, China"}]},{"given":"E","family":"Tang","sequence":"additional","affiliation":[{"name":"College of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, China"}]}],"member":"179","published-online":{"date-parts":[[2020,7,29]]},"reference":[{"issue":"9","key":"e_1_3_1_2_2","first-page":"1335","article-title":"Person re-identification by multi-channel parts-based CNN with improved triplet loss function","volume":"12","author":"Cheng D.","year":"2016","unstructured":"ChengD., GongY., ZhouS., WangJ., ZhengN., Person re-identification by multi-channel parts-based CNN with improved triplet loss function, In 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