Bi-level Relative Information Analysis for Multiple-Shot Person Re-Identification

Wei LI; Yang WU; Masayuki MUKUNOKI; Michihiko MINOH

doi:10.1587/transinf.E96.D.2450

Bi-level Relative Information Analysis for Multiple-Shot Person Re-Identification

Wei LI, Yang WU, Masayuki MUKUNOKI, Michihiko MINOH

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Multiple-shot person re-identification, which is valuable for application in visual surveillance, tackles the problem of building the correspondence between images of the same person from different cameras. It is challenging because of the large within-class variations due to the changeable body appearance and environment and the small between-class differences arising from the possibly similar body shape and clothes style. A novel method named “Bi-level Relative Information Analysis” is proposed in this paper for the issue by treating it as a set-based ranking problem. It creatively designs a relative dissimilarity using set-level neighborhood information, called “Set-level Common-Near-Neighbor Modeling”, complementary to the sample-level relative feature “Third-Party Collaborative Representation” which has recently been proven to be quite effective for multiple-shot person re-identification. Experiments implemented on several public benchmark datasets show significant improvements over state-of-the-art methods.

Publication: IEICE TRANSACTIONS on Information Vol.E96-D No.11 pp.2450-2461

Publication Date: 2013/11/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1587/transinf.E96.D.2450

Type of Manuscript: PAPER

Category: Image Recognition, Computer Vision

Authors

Wei LI
  Kyoto University
Yang WU
  Kyoto University
Masayuki MUKUNOKI
  Kyoto University
Michihiko MINOH
  Kyoto University

Keyword

Bi-level relative information analysis; multiple-shot person re-identification; visual surveillance

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Wei LI, Yang WU, Masayuki MUKUNOKI, Michihiko MINOH, "Bi-level Relative Information Analysis for Multiple-Shot Person Re-Identification" in IEICE TRANSACTIONS on Information, vol. E96-D, no. 11, pp. 2450-2461, November 2013, doi: 10.1587/transinf.E96.D.2450.
Abstract: Multiple-shot person re-identification, which is valuable for application in visual surveillance, tackles the problem of building the correspondence between images of the same person from different cameras. It is challenging because of the large within-class variations due to the changeable body appearance and environment and the small between-class differences arising from the possibly similar body shape and clothes style. A novel method named “Bi-level Relative Information Analysis” is proposed in this paper for the issue by treating it as a set-based ranking problem. It creatively designs a relative dissimilarity using set-level neighborhood information, called “Set-level Common-Near-Neighbor Modeling”, complementary to the sample-level relative feature “Third-Party Collaborative Representation” which has recently been proven to be quite effective for multiple-shot person re-identification. Experiments implemented on several public benchmark datasets show significant improvements over state-of-the-art methods.
URL: https://globals.ieice.org/en_transactions/information/10.1587/transinf.E96.D.2450/_p

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@ARTICLE{e96-d_11_2450,
author={Wei LI, Yang WU, Masayuki MUKUNOKI, Michihiko MINOH, },
journal={IEICE TRANSACTIONS on Information},
title={Bi-level Relative Information Analysis for Multiple-Shot Person Re-Identification},
year={2013},
volume={E96-D},
number={11},
pages={2450-2461},
abstract={Multiple-shot person re-identification, which is valuable for application in visual surveillance, tackles the problem of building the correspondence between images of the same person from different cameras. It is challenging because of the large within-class variations due to the changeable body appearance and environment and the small between-class differences arising from the possibly similar body shape and clothes style. A novel method named “Bi-level Relative Information Analysis” is proposed in this paper for the issue by treating it as a set-based ranking problem. It creatively designs a relative dissimilarity using set-level neighborhood information, called “Set-level Common-Near-Neighbor Modeling”, complementary to the sample-level relative feature “Third-Party Collaborative Representation” which has recently been proven to be quite effective for multiple-shot person re-identification. Experiments implemented on several public benchmark datasets show significant improvements over state-of-the-art methods.},
keywords={},
doi={10.1587/transinf.E96.D.2450},
ISSN={1745-1361},
month={November},}

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TY - JOUR
TI - Bi-level Relative Information Analysis for Multiple-Shot Person Re-Identification
T2 - IEICE TRANSACTIONS on Information
SP - 2450
EP - 2461
AU - Wei LI
AU - Yang WU
AU - Masayuki MUKUNOKI
AU - Michihiko MINOH
PY - 2013
DO - 10.1587/transinf.E96.D.2450
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E96-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2013
AB - Multiple-shot person re-identification, which is valuable for application in visual surveillance, tackles the problem of building the correspondence between images of the same person from different cameras. It is challenging because of the large within-class variations due to the changeable body appearance and environment and the small between-class differences arising from the possibly similar body shape and clothes style. A novel method named “Bi-level Relative Information Analysis” is proposed in this paper for the issue by treating it as a set-based ranking problem. It creatively designs a relative dissimilarity using set-level neighborhood information, called “Set-level Common-Near-Neighbor Modeling”, complementary to the sample-level relative feature “Third-Party Collaborative Representation” which has recently been proven to be quite effective for multiple-shot person re-identification. Experiments implemented on several public benchmark datasets show significant improvements over state-of-the-art methods.
ER -