Low-Rank Image Set Representation and Classification

• Published in: Advances in Brain Inspired Cognitive Systems pp. 321 - 330
• Main Authors: Cao, Youxia, Jiang, Bo, Chen, Zhuqiang, Tang, Jin, Luo, Bin
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 13.11.2016
• Series: Lecture Notes in Computer Science
• Subjects: Image set classification; Linear discriminant analysis; Low-rank representation
• ISBN: 9783319496849; 3319496840
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-49685-6_29

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.