Abstract
In this chapter, we introduce the problem of identity inference as a generalization of person re-identification. It is most appropriate to distinguish identity inference from re-identification in situations where a large number of observations must be identified without knowing a priori that groups of test images represent the same individual. The standard single- and multishot person re-identification common in the literature are special cases of our formulation. We present an approach to solving identity inference by modeling it as a labeling problem in a Conditional Random Field (CRF). The CRF model ensures that the final labeling gives similar labels to detections that are similar in feature space. Experimental results are given on the ETHZ, i-LIDS and CAVIAR datasets. Our approach yields state-of-the-art performance for multishot re-identification, and our results on the more general identity inference problem demonstrate that we are able to infer the identity of very many examples even with very few labeled images in the gallery.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
We prefer the SvsAll terminology as the SvsS terminology has been misinterpreted at least once in the literature.
References
Bak, S., Corvee, E., Bremond, F., Thonnat, M.: Multiple-shot human re-identification by mean riemannian covariance grid. In: Proceedings of AVSS, pp. 179–184 (2011)
Bazzani, L., Cristani, M., Murino, V.: Symmetry-driven accumulation of local features for human characterization and re-identification. Comput. Vis. Image Underst. 117(2), 130–144 (2013)
Bazzani, L., Cristani, M., Perina, A., Farenzena, M., Murino, V.: Multiple-shot person re-identification by hpe signature. In: 20th International Conference on Pattern Recognition, pp. 1413–1416 (2010)
Bazzani, L., Cristani, M., Perina, A., Murino, V.: Multiple-shot person re-identification by chromatic and epitomic analyses. Pattern Recogn. Lett. 33(7), 898–903 (2012)
Boix, X., Gonfaus, J.M., van de Weijer, J., Bagdanov, A.D., Serrat, J., Gonzà lez, J.: Harmony potentials. Int. J. Comput. Vision 96(1), 83–102 (2012)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23(11), 1222–1239 (2001)
Cai, Y., Pietikäinen, M.: Person re-identification based on global color context. In: Proceedings of the Asian Conference on Computer Vision Workshops, pp. 205–215 (2011)
Cheng, D.S., Cristani, M., Stoppa, M., Bazzani, L., Murino, V.: Custom pictorial structures for re-identification. In: Proceedings of the British Machine Vision Conference, vol. 2, p. 6 (2011)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of the IEEE Conference on Computer Vision and, Pattern Recognition, vol. 1, pp. 886–893, 2005
Dikmen, M., Akbas, E., Huang, T.S., Ahuja, N.: Pedestrian recognition with a learned metric. In: Proceedings of the Asian conference on Computer Vision, pp. 501–512 (2011)
Farenzena, M., Bazzani, L., Perina, A., Murino, V., Cristani, M.: Person re-identification by symmetry-driven accumulation of local features. In: Proceedings of the IEEE Conference on Computer Vision and, Pattern Recognition, pp. 2360–2367 (2010)
Felzenszwalb, P., Huttenlocher, D.: Efficient belief propagation for early vision. Int. J. Comput. Vision 70(1), 41–54 (2006)
Gray, D., Tao, H.: Viewpoint invariant pedestrian recognition with an ensemble of localized features. In: Proceedings of the European Conference on Computer Vision, pp. 262–275 (2008)
Karaman, S., Bagdanov, A.D.: Identity inference: generalizing person re-identification scenarios. In: Computer Vision. Workshops and Demonstrations, pp. 443–452. Springer, Heidelberg (2012)
Kolmogorov, V., Zabin, R.: What energy functions can be minimized via graph cuts? IEEE Trans. Pattern Anal. Mach. Intell. 26(2), 147–159 (2004)
Köstinger, M., Hirzer, M., Wohlhart, P., Roth, P.M., Bischof, H.: Large scale metric learning from equivalence constraints. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2012)
Loy, C.C., Liu, C., Gong, S.: Person re-identification by manifold ranking. In: Proceedings of IEEE International Conference on Image Processing (2013)
Prosser, B., Zheng, W., Gong, S., Xiang, T.: Person re-identification by support vector ranking. In: Proceedings of the British Machine Vision Conference (2010)
Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vision 47(1), 7–42 (2002)
Schwartz, W.R., Davis, L.S.: Learning discriminative appearance-based models using partial least squares. In: Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI), pp. 322–329. IEEE, New York (2009)
Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M., Rother, C.: A comparative study of energy minimization methods for markov random fields with smoothness-based priors. IEEE Trans. Pattern Anal. Mach. Intell. 30(6), 1068–1080 (2008)
Vedaldi, A., Zisserman, A.: Efficient additive kernels via explicit feature maps. IEEE Trans. Pattern Anal. Mach. Intell. 34(3), 480–492 (2012)
Wainwright, M., Jaakkola, T., Willsky, A.: Map estimation via agreement on trees: message-passing and linear programming. IEEE Trans. Inf. Theory 51(11), 3697–3717 (2005)
Zheng, W., Gong, S., Xiang, T.: Associating groups of people. In: Proceedings of British Machine Vision Conference (2009)
Zheng, W., Gong, S., Xiang, T.: Re-identification by relative distance comparison.IEEE Trans. Pattern Anal. Mach. Intell. PP(99), 1 (2012)
Zheng, W., Gong, S., Xiang, T.: Transfer re-identification: From person to set-based verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2012)
Zhou, D., Weston, J., Gretton, A., Bousquet, O., Schölkopf, B.: Ranking on data manifolds. Adv. Neural Inf. Proc. Syst. 16, 169–176 (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag London
About this chapter
Cite this chapter
Karaman, S., Lisanti, G., Bagdanov, A.D., Bimbo, A.D. (2014). From Re-identification to Identity Inference: Labeling Consistency by Local Similarity Constraints. In: Gong, S., Cristani, M., Yan, S., Loy, C. (eds) Person Re-Identification. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-6296-4_14
Download citation
DOI: https://doi.org/10.1007/978-1-4471-6296-4_14
Published:
Publisher Name: Springer, London
Print ISBN: 978-1-4471-6295-7
Online ISBN: 978-1-4471-6296-4
eBook Packages: Computer ScienceComputer Science (R0)