A Probabilistic Collaborative Representation Based Approach for Pattern Classification

Sijia Cai, Lei Zhang, Wangmeng Zuo, Xiangchu Feng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

208 Citations (Scopus)

Abstract

Conventional representation based classifiers, ranging from the classical nearest neighbor classifier and nearest subspace classifier to the recently developed sparse representation based classifier (SRC) and collaborative representation based classifier (CRC), are essentially distance based classifiers. Though SRC and CRC have shown interesting classification results, their intrinsic classification mechanism remains unclear. In this paper we propose a probabilistic collaborative representation framework, where the probability that a test sample belongs to the collaborative subspace of all classes can be well defined and computed. Consequently, we present a probabilistic collaborative representation based classifier (ProCRC), which jointly maximizes the likelihood that a test sample belongs to each of the multiple classes. The final classification is performed by checking which class has the maximum likelihood. The proposed ProCRC has a clear probabilistic interpretation, and it shows superior performance to many popular classifiers, including SRC, CRC and SVM. Coupled with the CNN features, it also leads to state-of-the-art classification results on a variety of challenging visual datasets.

Original languageEnglish
Title of host publicationProceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
PublisherIEEE Computer Society
Pages2950-2959
Number of pages10
ISBN (Electronic)9781467388504
DOIs
Publication statusPublished - 9 Dec 2016
Event29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 - Las Vegas, United States
Duration: 26 Jun 20161 Jul 2016

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume2016-December
ISSN (Print)1063-6919

Conference

Conference29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
Country/TerritoryUnited States
CityLas Vegas
Period26/06/161/07/16

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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