NDLI: Person re-identification by modelling principal component analysis coefficients of image dissimilarities

Content Provider	IET Digital Library
Author	Martinel, N. Micheloni, C.
Abstract	Signature-based matching has been the dominant choice for state-of-the-art person re-identification across multiple disjoint cameras. An approach that exploits image dissimilarities is proposed, treating re-identification as a binary classification problem. To achieve the objective, the person re-identification problem is addressed as follows: (i) first, compute the image dissimilarity between a pair of images acquired from two disjoint cameras; (ii) then learn the linear subspace where the image dissimilarities lie in an unsupervised fashion and (iii) lastly train a binary classifier in the linear subspace to discriminate between image dissimilarities computed for a positive pair (images are for the same person) and a negative pair (images are for different persons). An approach on two publicly available benchmark datasets is evaluated and compared with state-of-the-art methods for person re-identification.
Starting Page	1000
Ending Page	1001
Page Count	2
ISSN	00135194
Volume Number	50
e-ISSN	1350911X
Issue Number	Issue 14, Jul (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/50/14
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2014.0856
Journal	Electronics Letters
Publisher Date	2014-06-27
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Binary Classification Problem Computer Vision And Image Processing Technique Image Classification Image Dissimilarities Image Matching Image Recognition Image Sonsor Linear SubSpace Multiple Disjoint Cameras PCA Coefficient Modelling Person Reidentification Principal Component Analysis Signature-based Matching Statistics
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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