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2016 | OriginalPaper | Chapter

Low-Rank Image Set Representation and Classification

Authors : Youxia Cao, Bo Jiang, Zhuqiang Chen, Jin Tang, Bin Luo

Published in: Advances in Brain Inspired Cognitive Systems

Publisher: Springer International Publishing

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Abstract

Image set representation and classification is an important problem in computer vision and pattern recognition area. In real application, image set data often come with kinds of noises, corruptions or large errors which usually make the recognition/learning tasks of image set more challengeable. In this paper, we utilize the low-rank representation/component of image set to represent the observed image set which is called Low-rank Image Set Representation (LRISR). Comparing with original observed image set, LRISR is generally noiseless and thus can encourage more robust learning process. Based on LRISR, we then use covariate-relation graph to encode the geometric relationship between covariates/features of LRISR and thus extract description vectors for LRISR classification task. Experimental results on several datasets demonstrate the benefits of the proposed image set representation and classification method.

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Metadata
Title
Low-Rank Image Set Representation and Classification
Authors
Youxia Cao
Bo Jiang
Zhuqiang Chen
Jin Tang
Bin Luo
Copyright Year
2016
Book
Advances in Brain Inspired Cognitive Systems

Print ISBN: 978-3-319-49684-9

Electronic ISBN: 978-3-319-49685-6

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-49685-6_29

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