Multi-shot person re-identification via relational stein divergence

Alavi, Azadeh, Yang, Yan, Harandi, Mehrtash, & Sanderson, Conrad (2013) Multi-shot person re-identification via relational stein divergence. In ICIP 2013 Proceedings : 2013 IEEE International Conference on Image Processing, Institute of Electrical and Electronics Engineers, Inc., Melbourne Convention and Exhibition Centre, Melbourne, pp. 3542-3546.

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Abstract

Person re-identification is particularly challenging due to significant appearance changes across separate camera views. In order to re-identify people, a representative human signature should effectively handle differences in illumination, pose and camera parameters. While general appearance-based methods are modelled in Euclidean spaces, it has been argued that some applications in image and video analysis are better modelled via non-Euclidean manifold geometry. To this end, recent approaches represent images as covariance matrices, and interpret such matrices as points on Riemannian manifolds. As direct classification on such manifolds can be difficult, in this paper we propose to represent each manifold point as a vector of similarities to class representers, via a recently introduced form of Bregman matrix divergence known as the Stein divergence. This is followed by using a discriminative mapping of similarity vectors for final classification. The use of similarity vectors is in contrast to the traditional approach of embedding manifolds into tangent spaces, which can suffer from representing the manifold structure inaccurately. Comparative evaluations on benchmark ETHZ and iLIDS datasets for the person re-identification task show that the proposed approach obtains better performance than recent techniques such as Histogram Plus Epitome, Partial Least Squares, and Symmetry-Driven Accumulation of Local Features.

Impact and interest:

4 citations in Scopus
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3 citations in Web of Science®

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ID Code: 71704
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: surveillance, person re-identification, manifolds
DOI: 10.1109/ICIP.2013.6738731
ISBN: 9781479923410
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > APPLIED MATHEMATICS (010200)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Image Processing (080106)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Signal Processing (090609)
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: © 2013 by the Institute of Electrical and Electronics
Engineers, Inc.
Deposited On: 15 May 2014 22:36
Last Modified: 23 May 2014 00:59

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