{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T08:39:39Z","timestamp":1782376779752,"version":"3.54.5"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2018,7,19]],"date-time":"2018-07-19T00:00:00Z","timestamp":1531958400000},"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":["Multimed Tools Appl"],"published-print":{"date-parts":[[2019,3]]},"DOI":"10.1007\/s11042-018-6409-3","type":"journal-article","created":{"date-parts":[[2018,7,19]],"date-time":"2018-07-19T03:09:29Z","timestamp":1531969769000},"page":"5843-5861","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Cross-dataset person re-identification using deep convolutional neural networks: effects of context and domain adaptation"],"prefix":"10.1007","volume":"78","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0430-2789","authenticated-orcid":false,"given":"An\u0131l","family":"Gen\u00e7","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haz\u0131m Kemal","family":"Ekenel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2018,7,19]]},"reference":[{"key":"6409_CR1","doi-asserted-by":"crossref","unstructured":"Ahmed E, Jones M, Marks TK (2015) An improved deep learning architecture for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3908\u20133916","DOI":"10.1109\/CVPR.2015.7299016"},{"key":"6409_CR2","unstructured":"Barbosa IB, Cristani M, Caputo B, Rognhaugen A, Theoharis T (2017) Looking beyond appearances: synthetic training data for deep CNNs in re-identification. CoRR. arXiv:\n                    1701.03153"},{"key":"6409_CR3","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) ImageNet: a large-scale hierarchical image database. In: CVPR09","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"6409_CR4","unstructured":"Fan H, Zheng L, Yang Y (2017) Unsupervised person re-identification: clustering and fine-tuning. arXiv:\n                    1705.10444"},{"issue":"9","key":"6409_CR5","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","volume":"32","author":"PF Felzenszwalb","year":"2010","unstructured":"Felzenszwalb PF, Girshick RB, McAllester D, Ramanan D (2010) Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9):1627\u20131645","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"6409_CR6","first-page":"262","volume":"2008","author":"D Gray","year":"2008","unstructured":"Gray D, Tao H (2008) Viewpoint invariant pedestrian recognition with an ensemble of localized features. Comput. Vis.\u2013ECCV 2008:262\u2013275","journal-title":"Comput. Vis.\u2013ECCV"},{"key":"6409_CR7","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"6409_CR8","unstructured":"Hermans A, Beyer L, Leibe B (2017) In defense of the triplet loss for person re-identification. CoRR. arXiv:\n                    1703.07737"},{"key":"6409_CR9","doi-asserted-by":"crossref","unstructured":"Hirzer M, Beleznai C, Roth PM, Bischof H (2011) Person re-identification by descriptive and discriminative classification. In: Scandinavian conference on image analysis. Springer, pp 91\u2013102","DOI":"10.1007\/978-3-642-21227-7_9"},{"key":"6409_CR10","unstructured":"Hu Y, Yi D, Liao S, Lei Z, Li SZ (2014) Cross dataset person re-identification. In: Asian Conference on computer vision. Springer, pp 650\u2013664"},{"key":"6409_CR11","doi-asserted-by":"crossref","unstructured":"Hu J, Lu J, Tan YP (2015) Deep transfer metric learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 325\u2013333","DOI":"10.1109\/CVPR.2015.7298629"},{"key":"6409_CR12","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Weinberger KQ, van der Maaten L (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, vol 1, p 3","DOI":"10.1109\/CVPR.2017.243"},{"key":"6409_CR13","doi-asserted-by":"crossref","unstructured":"Koestinger M, Hirzer M, Wohlhart P, Roth PM, Bischof H (2012) Large scale metric learning from equivalence constraints. In: 2012 IEEE Conference on computer vision and pattern recognition (CVPR). IEEE, pp 2288\u20132295","DOI":"10.1109\/CVPR.2012.6247939"},{"key":"6409_CR14","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097\u20131105"},{"key":"6409_CR15","doi-asserted-by":"crossref","unstructured":"Li W, Zhao R, Xiao T, Wang X (2014) Deepreid: deep filter pairing neural network for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 152\u2013159","DOI":"10.1109\/CVPR.2014.27"},{"key":"6409_CR16","doi-asserted-by":"crossref","unstructured":"Liao S, Hu Y, Li S (2014) Joint dimension reduction and metric learning for person re-identification","DOI":"10.1109\/ICCV.2015.420"},{"key":"6409_CR17","unstructured":"Ma AJ, Yuen PC, Li J (2013) Domain transfer support vector ranking for person re-identification without target camera label information. In: Proceedings of the IEEE international conference on computer vision, pp 3567\u20133574"},{"issue":"5","key":"6409_CR18","doi-asserted-by":"publisher","first-page":"1599","DOI":"10.1109\/TIP.2015.2395715","volume":"24","author":"AJ Ma","year":"2015","unstructured":"Ma AJ, Li J, Yuen PC, Li P (2015) Cross-domain person reidentification using domain adaptation ranking svms. IEEE Trans Image Process 24(5):1599\u20131613","journal-title":"IEEE Trans Image Process"},{"key":"6409_CR19","doi-asserted-by":"publisher","unstructured":"McLaughlin N, Rincon JMD, Miller P (2015) Data-augmentation for reducing dataset bias in person re-identification. In: 2015 12th IEEE International conference on advanced video and signal based surveillance (AVSS), pp 1\u20136. \n                    https:\/\/doi.org\/10.1109\/AVSS.2015.7301739","DOI":"10.1109\/AVSS.2015.7301739"},{"issue":"4","key":"6409_CR20","doi-asserted-by":"publisher","first-page":"3885","DOI":"10.1007\/s11042-017-4875-7","volume":"78","author":"Aparajita Nanda","year":"2017","unstructured":"Nanda A, Chauhan DS, Sa KP, Bakshi S (2017) Illumination and scale invariant relevant visual features with hypergraph-based learning for multi-shot person re-identification. Multimedia Tools and Applications. \n                    https:\/\/doi.org\/10.1007\/s11042-017-4875-7","journal-title":"Multimedia Tools and Applications"},{"key":"6409_CR21","doi-asserted-by":"publisher","first-page":"6471","DOI":"10.1109\/ACCESS.2017.2686438","volume":"5","author":"A Nanda","year":"2017","unstructured":"Nanda A, Sa PK, Choudhury SK, Bakshi S, Majhi B (2017) A neuromorphic person re-identification framework for video surveillance. IEEE Access 5:6471\u20136482. \n                    https:\/\/doi.org\/10.1109\/ACCESS.2017.2686438","journal-title":"IEEE Access"},{"key":"6409_CR22","doi-asserted-by":"publisher","unstructured":"Peng P, Xiang T, Wang Y, Pontil M, Gong S, Huang T, Tian Y (2016) Unsupervised cross-dataset transfer learning for person re-identification. In: 2016 IEEE Conference on computer vision and pattern recognition (CVPR), pp 1306\u20131315. \n                    https:\/\/doi.org\/10.1109\/CVPR.2016.146","DOI":"10.1109\/CVPR.2016.146"},{"issue":"3","key":"6409_CR23","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211\u2013252","journal-title":"Int J Comput Vis"},{"key":"6409_CR24","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. In: International Conference on learning representations (ICLR)"},{"issue":"1","key":"6409_CR25","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton GE, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"6409_CR26","doi-asserted-by":"crossref","unstructured":"Sun Y, Zheng L, Deng W, Wang S (2017) Svdnet for pedestrian retrieval","DOI":"10.1109\/ICCV.2017.410"},{"key":"6409_CR27","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"6409_CR28","doi-asserted-by":"publisher","unstructured":"Torralba A, Efros AA (2011) Unbiased look at dataset bias. In: CVPR 2011, pp 1521\u20131528. \n                    https:\/\/doi.org\/10.1109\/CVPR.2011.5995347","DOI":"10.1109\/CVPR.2011.5995347"},{"key":"6409_CR29","doi-asserted-by":"publisher","unstructured":"Wu Q (2017) Multi-scale convolutional network for person re-identification. \n                    https:\/\/doi.org\/10.2991\/cnct-16.2017.115","DOI":"10.2991\/cnct-16.2017.115"},{"key":"6409_CR30","doi-asserted-by":"crossref","unstructured":"Xiao T, Li H, Ouyang W, Wang X (2016) Learning deep feature representations with domain guided dropout for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1249\u20131258","DOI":"10.1109\/CVPR.2016.140"},{"key":"6409_CR31","doi-asserted-by":"crossref","unstructured":"Yi D, Lei Z, Liao S, Li SZ (2014) Deep metric learning for person re-identification. In: 2014 22nd International conference on pattern recognition (ICPR). IEEE, pp 34\u201339","DOI":"10.1109\/ICPR.2014.16"},{"key":"6409_CR32","unstructured":"Yosinski J, Clune J, Bengio Y, Lipson H (2014) How transferable are features in deep neural networks? In: Advances in neural information processing systems, pp 3320\u20133328"},{"key":"6409_CR33","doi-asserted-by":"crossref","unstructured":"Zheng L, Shen L, Tian L, Wang S, Wang J, Tian Q (2015) Scalable person re-identification: a benchmark. In: IEEE International conference on computer vision","DOI":"10.1109\/ICCV.2015.133"},{"key":"6409_CR34","unstructured":"Zheng L, Yang Y, Hauptmann AG (2016) Person re-identification: past, present and future. CoRR. arXiv:\n                    1610.02984"},{"key":"6409_CR35","doi-asserted-by":"crossref","unstructured":"Zheng Z, Zheng L, Yang Y (2017) Unlabeled samples generated by gan improve the person re-identification baseline in vitro CoRR","DOI":"10.1109\/ICCV.2017.405"},{"key":"6409_CR36","doi-asserted-by":"crossref","unstructured":"Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2921\u20132929","DOI":"10.1109\/CVPR.2016.319"},{"key":"6409_CR37","doi-asserted-by":"crossref","unstructured":"Zhu JY, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks","DOI":"10.1109\/ICCV.2017.244"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-018-6409-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11042-018-6409-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-018-6409-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,7,18]],"date-time":"2019-07-18T19:21:57Z","timestamp":1563477717000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11042-018-6409-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,19]]},"references-count":37,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2019,3]]}},"alternative-id":["6409"],"URL":"https:\/\/doi.org\/10.1007\/s11042-018-6409-3","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,7,19]]},"assertion":[{"value":"14 October 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 July 2018","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 July 2018","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 July 2018","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}