{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T04:53:24Z","timestamp":1726030404358},"publisher-location":"Cham","reference-count":36,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030208899"},{"type":"electronic","value":"9783030208905"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-20890-5_25","type":"book-chapter","created":{"date-parts":[[2019,6,1]],"date-time":"2019-06-01T11:18:34Z","timestamp":1559387914000},"page":"381-397","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Ranking Loss: A Novel Metric Learning Method for Person Re-identification"],"prefix":"10.1007","author":[{"given":"Min","family":"Cao","sequence":"first","affiliation":[]},{"given":"Chen","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Xiyuan","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Silong","family":"Peng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,6,2]]},"reference":[{"key":"25_CR1","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.patcog.2017.03.023","volume":"75","author":"Z Zhao","year":"2017","unstructured":"Zhao, Z., Zhao, B., Su, F.: Person re-identification via integrating patch-based metric learning and local salience learning. Pattern Recogn. 75, 90\u201398 (2017)","journal-title":"Pattern Recogn."},{"key":"25_CR2","doi-asserted-by":"crossref","unstructured":"Chen, W., Chen, X., Zhang, J., Huang, K.: Beyond triplet loss: a deep quadruplet network for person re-identification. In: Computer Vision and Pattern Recognition, vol. 2 (2017)","DOI":"10.1109\/CVPR.2017.145"},{"issue":"1","key":"25_CR3","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1109\/TIP.2016.2619261","volume":"26","author":"C Sun","year":"2016","unstructured":"Sun, C., Wang, D., Lu, H.: Person re-identification via distance metric learning with latent variables. IEEE Trans. Image Process. 26(1), 23\u201334 (2016)","journal-title":"IEEE Trans. Image Process."},{"issue":"99","key":"25_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TMM.2017.2755983","volume":"PP","author":"S Zhou","year":"2017","unstructured":"Zhou, S., Wang, J., Shi, R., Hou, Q., Gong, Y., Zheng, N.: Large margin learning in set to set similarity comparison for person re-identification. IEEE Trans. Multimed. PP(99), 1\u20131 (2017)","journal-title":"IEEE Trans. Multimed."},{"key":"25_CR5","doi-asserted-by":"crossref","unstructured":"Jurie, F., Mignon, A.: PCCA: a new approach for distance learning from sparse pairwise constraints. In: Computer Vision and Pattern Recognition, pp. 2666\u20132672 (2012)","DOI":"10.1109\/CVPR.2012.6247987"},{"key":"25_CR6","doi-asserted-by":"crossref","unstructured":"Li, Z., Chang, S., Liang, F., Huang, T.S., Cao, L., Smith, J.R.: Learning locally-adaptive decision functions for person verification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3610\u20133617 (2013)","DOI":"10.1109\/CVPR.2013.463"},{"issue":"10","key":"25_CR7","doi-asserted-by":"publisher","first-page":"2993","DOI":"10.1016\/j.patcog.2015.04.005","volume":"48","author":"S Ding","year":"2015","unstructured":"Ding, S., Lin, L., Wang, G., Chao, H.: Deep feature learning with relative distance comparison for person re-identification. Pattern Recogn. 48(10), 2993\u20133003 (2015)","journal-title":"Pattern Recogn."},{"key":"25_CR8","doi-asserted-by":"crossref","unstructured":"Farenzena, M., Bazzani, L., Perina, A., Murino, V., Cristani, M.: Person re-identification by symmetry-driven accumulation of local features. In: Computer Vision and Pattern Recognition, pp. 2360\u20132367 (2010)","DOI":"10.1109\/CVPR.2010.5539926"},{"key":"25_CR9","doi-asserted-by":"crossref","unstructured":"Liao, S., Hu, Y., Zhu, X., Li, S.Z.: Person re-identification by local maximal occurrence representation and metric learning. In: Computer Vision and Pattern Recognition, pp. 2197\u20132206 (2015)","DOI":"10.1109\/CVPR.2015.7298832"},{"key":"25_CR10","doi-asserted-by":"crossref","unstructured":"Su, C., Li, J., Zhang, S., Xing, J., Gao, W., Tian, Q.: Pose-driven deep convolutional model for person re-identification. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 3980\u20133989. IEEE (2017)","DOI":"10.1109\/ICCV.2017.427"},{"key":"25_CR11","doi-asserted-by":"crossref","unstructured":"Pedagadi, S., Orwell, J., Velastin, S., Boghossian, B.: Local fisher discriminant analysis for pedestrian re-identification. In: Computer Vision and Pattern Recognition, pp. 3318\u20133325 (2013)","DOI":"10.1109\/CVPR.2013.426"},{"key":"25_CR12","doi-asserted-by":"crossref","unstructured":"Cao, M., Chen, C., Hu, X., Peng, S.: From groups to co-traveler sets: pair matching based person re-identification framework. In: IEEE International Conference on Computer Vision Workshop, pp. 2573\u20132582 (2017)","DOI":"10.1109\/ICCVW.2017.302"},{"key":"25_CR13","doi-asserted-by":"crossref","unstructured":"Chen, C., Cao, M., Hu, X., Peng, S.: Key person aided re-identification in partially ordered pedestrian set. In: Conference the British Machine Vision Conference (2017)","DOI":"10.5244\/C.31.132"},{"key":"25_CR14","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Zheng, L., Cao, D., Li, S.: Re-ranking person re-identification with k-reciprocal encoding. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3652\u20133661. IEEE (2017)","DOI":"10.1109\/CVPR.2017.389"},{"key":"25_CR15","unstructured":"Karanam, S., Gou, M., Wu, Z., Rates-Borras, A., Camps, O., Radke, R.J.: A systematic evaluation and benchmark for person re-identification: Features, metrics, and datasets. IEEE Trans. Pattern Anal. Mach. Intell., 1 (2016)"},{"key":"25_CR16","doi-asserted-by":"crossref","unstructured":"Zhang, L., Xiang, T., Gong, S.: Learning a discriminative null space for person re-identification. In: Computer Vision and Pattern Recognition, pp. 1239\u20131248 (2016)","DOI":"10.1109\/CVPR.2016.139"},{"key":"25_CR17","doi-asserted-by":"crossref","unstructured":"Wang, F., Zuo, W., Lin, L., Zhang, D., Zhang, L.: Joint learning of single-image and cross-image representations for person re-identification. In: Computer Vision and Pattern Recognition, pp. 1288\u20131296 (2016)","DOI":"10.1109\/CVPR.2016.144"},{"key":"25_CR18","doi-asserted-by":"crossref","unstructured":"Zhou, S., Wang, J., Wang, J., Gong, Y., Zheng, N.: Point to set similarity based deep feature learning for person re-identification. In: Computer Vision and Pattern Recognition, pp. 5028\u20135037 (2017)","DOI":"10.1109\/CVPR.2017.534"},{"key":"25_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1007\/978-3-319-46478-7_31","volume-title":"Computer Vision \u2013 ECCV 2016","author":"Y Wen","year":"2016","unstructured":"Wen, Y., Zhang, K., Li, Z., Qiao, Y.: A discriminative feature learning approach for deep face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 499\u2013515. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46478-7_31"},{"key":"25_CR20","unstructured":"Gray, D., Brennan, S., Tao, H.: Evaluating appearance models for recognition, reacquisition, and tracking. In: IEEE International Workshop on Performance Evaluation for Tracking and Surveillance (PETS) (2007)"},{"key":"25_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1007\/978-3-642-37331-2_3","volume-title":"Computer Vision \u2013 ACCV 2012","author":"W Li","year":"2013","unstructured":"Li, W., Zhao, R., Wang, X.: Human reidentification with transferred metric learning. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012. LNCS, vol. 7724, pp. 31\u201344. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-37331-2_3"},{"key":"25_CR22","doi-asserted-by":"crossref","unstructured":"Matsukawa, T., Okabe, T., Suzuki, E., Sato, Y.: Hierarchical Gaussian descriptor for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1363\u20131372 (2016)","DOI":"10.1109\/CVPR.2016.152"},{"issue":"2","key":"25_CR23","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1109\/TPAMI.2017.2666805","volume":"40","author":"Y-C Chen","year":"2018","unstructured":"Chen, Y.-C., Zhu, X., Zheng, W.-S., Lai, J.-H.: Person re-identification by camera correlation aware feature augmentation. IEEE Trans. Pattern Anal. Mach. Intell. 40(2), 392\u2013408 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"25_CR24","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, pp. 1097\u20131105 (2012)"},{"key":"25_CR25","doi-asserted-by":"crossref","unstructured":"Tian, M., et al.: Eliminating background-bias for robust person re-identification. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018","DOI":"10.1109\/CVPR.2018.00607"},{"key":"25_CR26","doi-asserted-by":"crossref","unstructured":"Ahmed, E., Jones, M., Marks, T.K.: An improved deep learning architecture for person re-identification. In: Computer Vision and Pattern Recognition, pp. 3908\u20133916 (2015)","DOI":"10.1109\/CVPR.2015.7299016"},{"issue":"5","key":"25_CR27","doi-asserted-by":"publisher","first-page":"2353","DOI":"10.1109\/TIP.2016.2545929","volume":"25","author":"SZ Chen","year":"2016","unstructured":"Chen, S.Z., Guo, C.C., Lai, J.: Deep ranking for person re-identification via joint representation learning. IEEE Trans. Image Process. 25(5), 2353\u20132367 (2016)","journal-title":"IEEE Trans. Image Process."},{"key":"25_CR28","doi-asserted-by":"crossref","unstructured":"Cheng, D., Gong, Y., Zhou, S., Wang, J., Zheng, N.: Person re-identification by multi-channel parts-based CNN with improved triplet loss function. In: Computer Vision and Pattern Recognition, pp. 1335\u20131344 (2016)","DOI":"10.1109\/CVPR.2016.149"},{"key":"25_CR29","doi-asserted-by":"crossref","unstructured":"Su, C., Li, J., Zhang, S., Xing, J., Gao, W., Tian, Q.: Pose-driven deep convolutional model for person re-identification. In: The IEEE International Conference on Computer Vision (ICCV), October 2017","DOI":"10.1109\/ICCV.2017.427"},{"key":"25_CR30","doi-asserted-by":"crossref","unstructured":"Zhao, L., Li, X., Zhuang, Y., Wang, J.: Deeply-learned part-aligned representations for person re-identification. In: The IEEE International Conference on Computer Vision (ICCV), October 2017","DOI":"10.1109\/ICCV.2017.349"},{"key":"25_CR31","doi-asserted-by":"crossref","unstructured":"Guo, Y., Cheung, N.-M.: Efficient and deep person re-identification using multi-level similarity. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018","DOI":"10.1109\/CVPR.2018.00248"},{"key":"25_CR32","doi-asserted-by":"crossref","unstructured":"Mao, C., Li, Y., Zhang, Y., Zhang, Z., Li, X.: Multi-channel pyramid person matching network for person re-identification (2018)","DOI":"10.1609\/aaai.v32i1.12225"},{"key":"25_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"875","DOI":"10.1007\/978-3-319-46454-1_53","volume-title":"Computer Vision \u2013 ECCV 2016","author":"C Jose","year":"2016","unstructured":"Jose, C., Fleuret, F.: Scalable metric learning via weighted approximate rank component analysis. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 875\u2013890. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46454-1_53"},{"key":"25_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"858","DOI":"10.1007\/978-3-319-46493-0_52","volume-title":"Computer Vision \u2013 ECCV 2016","author":"N Martinel","year":"2016","unstructured":"Martinel, N., Das, A., Micheloni, C., Roy-Chowdhury, A.K.: Temporal model adaptation for person re-identification. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 858\u2013877. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_52"},{"key":"25_CR35","doi-asserted-by":"crossref","unstructured":"Yang, X., Wang, M., Tao, D.: Person re-identification with metric learning using privileged information. IEEE Trans. Image Process. PP(99), 1 (2018)","DOI":"10.1109\/TIP.2017.2765836"},{"key":"25_CR36","doi-asserted-by":"crossref","unstructured":"Zhou, Q., et al.: Graph correspondence transfer for person re-identification (2018)","DOI":"10.1609\/aaai.v32i1.12241"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2018"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-20890-5_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,18]],"date-time":"2022-09-18T22:43:54Z","timestamp":1663541034000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-20890-5_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030208899","9783030208905"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-20890-5_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"2 June 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Perth, WA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 December 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"accv2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/accv2018.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"979","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"274","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"28% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"2.7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}}]}}