Abstract
Person re-identification, aiming to identify images of the same person from various cameras configured in difference places, has attracted plenty of attention in the multimedia community. Previous work mainly focuses on feature presentation and distance measure, and achieves promising results on some standard databases. However, the performance is still not good enough due to appearance changes caused by variations in illuminations, poses, viewpoints and occlusion. This paper addresses the problem through result re-ranking by introducing user feedback. In particular, considering the peculiarity of scarce positive and global similar negative samples in the person re-identification problem, we propose a region-based interactive ranking optimization method, to improve the original query result by labeling locally similar and dissimilar image regions. Experiments conducted on two standard data sets have validated the effectiveness of the proposed method with an average improvement of 10-30% over original basic method. It is proved that the ranking optimization algorithm is both an effective and efficient method to improve the original person re-identification result.
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
Preview
Unable to display preview. Download preview PDF.
References
Ali, S., Javed, O., Haering, N., Kanade, T.: Interactive retrieval of targets for wide area surveillance. In: ACM International Conference on Multimedia (ACM MM) (2010)
Farenzena, M., Bazzani, L., Perina, A., Murino, V., Cristani, M.: Person re-identification by symmetry-driven accumulation of local features. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2010)
Zheng, W.-S., Gong, S., Xiang, T.: Person re-identification by probabilistic relative distance comparison. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2011)
Li, X., Tao, D., Jin, L., Wang, Y., Yuan, Y.: Person re-identification by regularized smoothing kiss metric learning. IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) (2013)
Layne, R., Hospedales, T.M., Gong, S.: Person Re-identification by Attributes. In: British Machine Vision Conference (BMVC) (2012)
Kviatkovsky, I., Adam, A., Rivlin, E.: Color invariants for person reidentification. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) (2013)
Rui, Z., Wanli, O., Xiaogang, W.: Unsupervised salience learning for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2013)
Kostinger, M., Hirzer, M., Wohlhart, P., Roth, P., Bischof, H.: Large scale metric learning from equivalence constraints. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2012)
Leng, Q., Hu, R., Liang, C.: Bidirectional ranking for person re-identification. In: IEEE International Conference on Multimedia and Expo (ICME) (2013)
Hirzer, M., Beleznai, C., Roth, P.M., Bischof, H.: Person re-identification by descriptive and discriminative classification. In: Heyden, A., Kahl, F. (eds.) SCIA 2011. LNCS, vol. 6688, pp. 91–102. Springer, Heidelberg (2011)
Kherfi, M.L., Ziou, D.: Relevance feedback for CBIR: a new approach based on probabilistic feature weighting with positive and negative examples. IEEE Transactions on Image Processing (TIP) (2006)
Liu, C., Loy, C.C., Gong, S.: POP: Person re-identification post-rank optimisation. In: IEEE International Conference on Computer Vision (ICCV) (2013)
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, Part I. LNCS, vol. 7724, pp. 31–44. Springer, Heidelberg (2013)
Gray, S.B.D., Tao, H.: Evaluating appearance models for recognition, reacquisition, and tracking. In: IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS) (2007)
Wang, X., Doretto, G., Sebastian, T., Rittscher, J., Tu, P.: Shape and appearance context modeling. In: IEEE International Conference on Computer Vision (ICCV) (2007)
Parkash, A., Parikh, D.: Attributes for classifier feedback. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 354–368. Springer, Heidelberg (2012)
BNauml, M., Fischer, M., Bernardin, K., Ekenel, H.K., Stiefelhagen, R.: Interactive person-retrieval in tv series and distributed surveillance video. In: ACM International Conference on Multimedia (ACM MM) (2010)
Fischer, M., Ekenel, H.K., Stiefelhagen, R.: Interactive person re-identification in tv series. In: CBMI (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Wang, Z., Hu, R., Liang, C., Leng, Q., Sun, K. (2014). Region-Based Interactive Ranking Optimization for Person Re-identification. In: Ooi, W.T., Snoek, C.G.M., Tan, H.K., Ho, CK., Huet, B., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2014. PCM 2014. Lecture Notes in Computer Science, vol 8879. Springer, Cham. https://doi.org/10.1007/978-3-319-13168-9_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-13168-9_1
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13167-2
Online ISBN: 978-3-319-13168-9
eBook Packages: Computer ScienceComputer Science (R0)