University of Oulu

X. Liu et al., "A joint optimization framework of low-dimensional projection and collaborative representation for discriminative classification," 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, 2018, pp. 1493-1498. doi: 10.1109/ICPR.2018.8545267

A joint optimization framework of low-dimensional projection and collaborative representation for discriminative classification

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Author: Liu, Xiaofeng1,2,3; Li, Zhaofeng2,3,4; Kong, Lingsheng2;
Organizations: 1Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, USA
2Changchun institute of optical precision machinery and physics, Chinese academy of sciences, CAS, Changchun, China
3University of Chinese Academy of Sciences, Beijing, China
4Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland
5Viterbi school of engineering, University of Southern California, Los Angeles, USA
6Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.8 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2019062722214
Language: English
Published: Institute of Electrical and Electronics Engineers, 2018
Publish Date: 2019-06-27
Description:

Abstract

Various representation-based methods have been developed and shown great potential for pattern classification. To further improve their discriminability, we propose a Bi-level optimization framework in terms of both low-dimensional projection and collaborative representation. Specifically, during the projection phase, we try to minimize the intra-class similarity and inter-class dissimilarity, while in the representation phase, our goal is to achieve the lowest correlation of the representation results. Solving this joint optimization mutually reinforces both aspects of feature projection and representation. Experiments on face recognition, object categorization and scene classification dataset demonstrate remarkable performance improvements led by the proposed framework.

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ISBN: 978-1-5386-3788-3
ISBN Print: 978-1-5386-3789-0
Pages: 1493 - 1498
DOI: 10.1109/ICPR.2018.8545267
OADOI: https://oadoi.org/10.1109/ICPR.2018.8545267
Host publication: 2018 24th International Conference on Pattern Recognition (ICPR)
Conference: International Conference on Pattern Recognition
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Subjects:
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