IEICE TRANSACTIONS on Information
Adaptive Metric Learning for People Re-Identification
Summary :
There exist two intrinsic issues in multiple-shot person re-identification: (1) large differences in camera view, illumination, and non-rigid deformation of posture that make the intra-class variance even larger than the inter-class variance; (2) only a few training data that are available for learning tasks in a realistic re-identification scenario. In our previous work, we proposed a local distance comparison framework to deal with the first issue. In this paper, to deal with the second issue (i.e., to derive a reliable distance metric from limited training data), we propose an adaptive learning method to learn an adaptive distance metric, which integrates prior knowledge learned from a large existing auxiliary dataset and task-specific information extracted from a much smaller training dataset. Experimental results on several public benchmark datasets show that combined with the local distance comparison framework, our adaptive learning method is superior to conventional approaches.
- Publication
- IEICE TRANSACTIONS on Information Vol.E97-D No.11 pp.2888-2902
- Publication Date
- 2014/11/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2013EDP7451
- Type of Manuscript
- PAPER
- Category
- Image Processing and Video Processing
Authors
Guanwen ZHANG
Nagoya University
Jien KATO
Nagoya University
Yu WANG
Nagoya University
Kenji MASE
Nagoya University
Keyword
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Guanwen ZHANG, Jien KATO, Yu WANG, Kenji MASE, "Adaptive Metric Learning for People Re-Identification" in IEICE TRANSACTIONS on Information,
vol. E97-D, no. 11, pp. 2888-2902, November 2014, doi: 10.1587/transinf.2013EDP7451.
Abstract: There exist two intrinsic issues in multiple-shot person re-identification: (1) large differences in camera view, illumination, and non-rigid deformation of posture that make the intra-class variance even larger than the inter-class variance; (2) only a few training data that are available for learning tasks in a realistic re-identification scenario. In our previous work, we proposed a local distance comparison framework to deal with the first issue. In this paper, to deal with the second issue (i.e., to derive a reliable distance metric from limited training data), we propose an adaptive learning method to learn an adaptive distance metric, which integrates prior knowledge learned from a large existing auxiliary dataset and task-specific information extracted from a much smaller training dataset. Experimental results on several public benchmark datasets show that combined with the local distance comparison framework, our adaptive learning method is superior to conventional approaches.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2013EDP7451/_p
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@ARTICLE{e97-d_11_2888,
author={Guanwen ZHANG, Jien KATO, Yu WANG, Kenji MASE, },
journal={IEICE TRANSACTIONS on Information},
title={Adaptive Metric Learning for People Re-Identification},
year={2014},
volume={E97-D},
number={11},
pages={2888-2902},
abstract={There exist two intrinsic issues in multiple-shot person re-identification: (1) large differences in camera view, illumination, and non-rigid deformation of posture that make the intra-class variance even larger than the inter-class variance; (2) only a few training data that are available for learning tasks in a realistic re-identification scenario. In our previous work, we proposed a local distance comparison framework to deal with the first issue. In this paper, to deal with the second issue (i.e., to derive a reliable distance metric from limited training data), we propose an adaptive learning method to learn an adaptive distance metric, which integrates prior knowledge learned from a large existing auxiliary dataset and task-specific information extracted from a much smaller training dataset. Experimental results on several public benchmark datasets show that combined with the local distance comparison framework, our adaptive learning method is superior to conventional approaches.},
keywords={},
doi={10.1587/transinf.2013EDP7451},
ISSN={1745-1361},
month={November},}
Copy
TY - JOUR
TI - Adaptive Metric Learning for People Re-Identification
T2 - IEICE TRANSACTIONS on Information
SP - 2888
EP - 2902
AU - Guanwen ZHANG
AU - Jien KATO
AU - Yu WANG
AU - Kenji MASE
PY - 2014
DO - 10.1587/transinf.2013EDP7451
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2014
AB - There exist two intrinsic issues in multiple-shot person re-identification: (1) large differences in camera view, illumination, and non-rigid deformation of posture that make the intra-class variance even larger than the inter-class variance; (2) only a few training data that are available for learning tasks in a realistic re-identification scenario. In our previous work, we proposed a local distance comparison framework to deal with the first issue. In this paper, to deal with the second issue (i.e., to derive a reliable distance metric from limited training data), we propose an adaptive learning method to learn an adaptive distance metric, which integrates prior knowledge learned from a large existing auxiliary dataset and task-specific information extracted from a much smaller training dataset. Experimental results on several public benchmark datasets show that combined with the local distance comparison framework, our adaptive learning method is superior to conventional approaches.
ER -
English
