Deep Part Features Learning by a Normalised Double-Margin-Based Contrastive Loss Function for Person Re-Identification

María José Gómez-Silva, José María Armingol, Arturo de la Escalera

2017

Abstract

The selection of discriminative features that properly define a person appearance is one of the current challenges for person re-identification. This paper presents a three-dimensional representation to compare person images, which is based on the similarity, independently measured for the head, upper body, and legs from two images. Three deep Siamese neural networks have been implemented to automatically find salient features for each body part. One of the main problems in the learning of features for re-identification is the presence of intra-class variations and inter-class ambiguities. This paper proposes a novel normalized double-margin-based contrastive loss function for the training of Siamese networks, which not only improves the robustness of the learned features against the mentioned problems but also reduce the training time. A comparative evaluation over the challenging PRID 2011 dataset has been conducted, resulting in a remarkable enhancement of the single-shot re-identification performance thanks to the use of our descriptor based on deeply learned features in comparison with the employment of low-level features. The obtained results also show the improvements generated by our normalized double-margin-based function with respect to the traditional contrastive loss function.

References

  1. Bazzani, L., Cristani, M. and Murino, V., 2013. Symmetrydriven accumulation of local features for human characterization and re-identification. Computer Vision and Image Understanding, 117(2), pp.130-144.
  2. Bazzani, L., Cristani, M. and Murino, V., 2014. SDALF: modeling human appearance with symmetry-driven accumulation of local features. Person ReIdentification, pp. 43-69. Springer London.
  3. Corvee, E., Bremond, F. and Thonnat, M., 2010. Person reidentification using spatial covariance regions of human body parts. Advanced Video and Signal Based Surveillance (AVSS), 2010 Seventh IEEE International Conference on, pp. 435-440. IEEE.
  4. Farenzena, M., Bazzani, L., Perina, A., Murino, V. and Cristani, M., 2010. Person re-identification by symmetry-driven accumulation of local features. Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pp. 2360-2367. IEEE.
  5. Hadsell, R., Chopra, S. and LeCun, Y., 2006. Dimensionality reduction by learning an invariant mapping. 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), Vol. 2, pp. 1735-1742. IEEE.
  6. Hirzer, M., Beleznai, C., Roth, P. M. and Bischof, H., 2011. Person re-identification by descriptive and discriminative classification. Scandinavian conference on Image analysis, pp. 91-102. Springer Berlin Heidelberg.
  7. Hirzer, M., Roth, P. M., Köstinger, M. and Bischof, H., 2012. Relaxed pairwise learned metric for person reidentification. European Conference on Computer Vision, pp. 780-793. Springer Berlin Heidelberg.
  8. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S. and Darrell, T., 2014. Caffe: Convolutional architecture for fast feature embedding. Proceedings of the 22nd ACM international conference on Multimedia, pp. 675-678. ACM.
  9. Krizhevsky, A., Sutskever, I. and Hinton, G. E., 2012. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, pp. 1097-1105.
  10. Layne, R., Hospedales, T. M. and Gong, S., 2014. Attributes-based re-identification. Person ReIdentification, pp. 93-117. Springer London.
  11. Ma, B., Su, Y. and Jurie, F., 2014. Discriminative Image Descriptors for Person Re-identification. Person ReIdentification, pp. 23-42. Springer London.
  12. Moon, H. and Phillips, P. J., 2001. Computational and performance aspects of PCA-based face-recognition algorithms. Perception, 30(3), 303-321.
  13. Oreifej, O., Mehran, R. and Shah, M. 2010. Human identity recognition in aerial images. Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pp. 709-716. IEEE.
  14. Rumelhart, D. E., Hinton, G. E. and Williams, R. J., 1988. Learning representations by back-propagating errors. Cognitive modeling, 5(3), 1.
  15. Sánchez, J., Perronnin, F., Mensink, T. and Verbeek, J., 2013. Image classification with the fisher vector: Theory and practice. International journal of computer vision, 105(3), pp.222-245.
  16. Yi, D., Lei, Z., Liao, S. and Li, S. Z., 2014. Deep Metric Learning for Person Re-identification. ICPR, Vol. 2014, pp. 34-39.
  17. Zhang, Y. and Li, S., 2011. Gabor-LBP based region covariance descriptor for person re-identification. Image and Graphics (ICIG), 2011 Sixth International Conference on, pp. 368-371. IEEE.
Download


Paper Citation


in Harvard Style

Gómez-Silva M., Armingol J. and de la Escalera A. (2017). Deep Part Features Learning by a Normalised Double-Margin-Based Contrastive Loss Function for Person Re-Identification . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-227-1, pages 277-285. DOI: 10.5220/0006167002770285


in Bibtex Style

@conference{visapp17,
author={María José Gómez-Silva and José María Armingol and Arturo de la Escalera},
title={Deep Part Features Learning by a Normalised Double-Margin-Based Contrastive Loss Function for Person Re-Identification},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={277-285},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006167002770285},
isbn={978-989-758-227-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)
TI - Deep Part Features Learning by a Normalised Double-Margin-Based Contrastive Loss Function for Person Re-Identification
SN - 978-989-758-227-1
AU - Gómez-Silva M.
AU - Armingol J.
AU - de la Escalera A.
PY - 2017
SP - 277
EP - 285
DO - 10.5220/0006167002770285