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de Madrid through SEGVAUTO-TRIES (S2013/MIT-
2713).
REFERENCES
Bazzani, L., Cristani, M., and Murino, V. (2013).
Symmetry-driven accumulation of local features for
human characterization and re-identification. Compu-
ter Vision and Image Understanding, 117(2):130–144.
Bazzani, L., Cristani, M., and Murino, V. (2014). Sdalf:
modeling human appearance with symmetry-driven
accumulation of local features. In Person Re-
Identification, pages 43–69. Springer.
Chan-Lang, S., Pham, Q. C., and Achard, C. (2016). Bidi-
rectional sparse representations for multi-shot person
re-identification. In Advanced Video and Signal Ba-
sed Surveillance (AVSS), 2016 13th IEEE Internatio-
nal Conference on, pages 263–270. IEEE.
Cheng, D. S. and Cristani, M. (2014). Person re-
identification by articulated appearance matching. In
Person Re-Identification, pages 139–160. Springer.
Felzenszwalb, P. F. and Huttenlocher, D. P. (2005). Pic-
torial structures for object recognition. International
journal of computer vision, 61(1):55–79.
Fisher, R. A. (1936). The use of multiple measurements in
taxonomic problems. Annals of eugenics, 7(2):179–
188.
G
´
omez-Silva, M. J., Armingol, J. M., and de la Escalera,
A. (2017). Deep part features learning by a normali-
sed double-margin-based contrastive loss function for
person re-identification. In VISIGRAPP (6: VISAPP),
pages 277–285.
Gray, D. and Tao, H. (2008). Viewpoint invariant pede-
strian recognition with an ensemble of localized fea-
tures. Computer Vision–ECCV 2008, pages 262–275.
Guillaumin, M., Verbeek, J., and Schmid, C. (2009). Is that
you? metric learning approaches for face identifica-
tion. In Computer Vision, 2009 IEEE 12th internatio-
nal conference on, pages 498–505. IEEE.
Hirzer, M., Beleznai, C., Roth, P. M., and Bischof, H.
(2011). Person re-identification by descriptive and
discriminative classification. In Scandinavian confe-
rence on Image analysis, pages 91–102. Springer.
Hirzer, M., Roth, P., K
¨
ostinger, M., and Bischof, H.
(2012). Relaxed pairwise learned metric for person
re-identification. Computer Vision–ECCV 2012, pa-
ges 780–793.
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J.,
Girshick, R., Guadarrama, S., and Darrell, T. (2014).
Caffe: Convolutional architecture for fast feature em-
bedding. In Proceedings of the 22nd ACM internatio-
nal conference on Multimedia, pages 675–678. ACM.
Khan, F. M. and Br
´
emond, F. (2016). Unsupervised data
association for metric learning in the context of multi-
shot person re-identification. In Advanced Video and
Signal Based Surveillance (AVSS), 2016 13th IEEE In-
ternational Conference on, pages 256–262. IEEE.
Koestinger, M., Hirzer, M., Wohlhart, P., Roth, P. M., and
Bischof, H. (2012). Large scale metric learning from
equivalence constraints. In Computer Vision and Pat-
tern Recognition (CVPR), 2012 IEEE Conference on,
pages 2288–2295. IEEE.
Layne, R., Hospedales, T. M., and Gong, S. (2014).
Attributes-based re-identification. In Person Re-
Identification, pages 93–117. Springer.
Liu, C., Gong, S., Loy, C. C., and Lin, X. (2014). Evaluating
feature importance for re-identification. In Person Re-
Identification, pages 203–228. Springer.
Ma, B., Su, Y., and Jurie, F. (2014). Discriminative image
descriptors for person re-identification. In Person Re-
Identification, pages 23–42. Springer.
Moon, H. and Phillips, P. J. (2001). Computational and
performance aspects of pca-based face-recognition al-
gorithms. Perception, 30(3):303–321.
Munaro, M., Fossati, A., Basso, A., Menegatti, E.,
and Van Gool, L. (2014). One-shot person re-
identification with a consumer depth camera. In Per-
son Re-Identification, pages 161–181. Springer.
Oreifej, O., Mehran, R., and Shah, M. (2010). Human iden-
tity recognition in aerial images. In Computer Vision
and Pattern Recognition (CVPR), 2010 IEEE Confe-
rence on, pages 709–716. IEEE.
Prosser, B., Zheng, W.-S., Gong, S., Xiang, T., and Mary,
Q. (2010). Person re-identification by support vector
ranking. In BMVC, volume 2, page 6.
Roth, P. M., Hirzer, M., K
¨
ostinger, M., Beleznai, C., and
Bischof, H. (2014). Mahalanobis distance learning for
person re-identification. In Person Re-Identification,
pages 247–267. Springer.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1988).
Learning representations by back-propagating errors.
Cognitive modeling, 5(3):1.
S
´
anchez, J., Perronnin, F., Mensink, T., and Verbeek, J.
(2013). Image classification with the fisher vector:
Theory and practice. International journal of com-
puter vision, 105(3):222–245.
Wei, S.-E., Ramakrishna, V., Kanade, T., and Sheikh, Y.
(2016). Convolutional pose machines. In Proceedings
of the IEEE Conference on Computer Vision and Pat-
tern Recognition, pages 4724–4732.
Yi, D., Lei, Z., Liao, S., and Li, S. Z. (2014). Deep me-
tric learning for person re-identification. In Pattern
Recognition (ICPR), 2014 22nd International Confe-
rence on, pages 34–39. IEEE.
Zhang, Y. and Li, S. (2011). Gabor-lbp based region covari-
ance descriptor for person re-identification. In Image
and Graphics (ICIG), 2011 Sixth International Confe-
rence on, pages 368–371. IEEE.
Zheng, W.-S., Gong, S., and Xiang, T. (2011). Person re-
identification by probabilistic relative distance com-
parison. In Computer vision and pattern recognition
(CVPR), 2011 IEEE conference on, pages 649–656.
IEEE.
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