Compressive Online Robust Principal Component Analysis with Multiple Prior Information

Huynh Van Luong, Nikos Deligiannis, Jürgen Seiler, Søren Forchhammer, André Kaup

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Online Robust Principle Component Analysis (RPCA) arises naturallyin time-varying signal decomposition problems such as videoforeground-background separation. We propose a compressive online RPCA algorithm that decomposes recursively a sequence of datavectors (e.g., frames) into sparse and low-rank components. Unlike conventional batch RPCA, which processes all the data directly, our method considers a small set of measurements taken per data vector (frame). Moreover, our method incorporates multiple prior information signals, namely previous reconstructed frames, to improve these paration and thereafter, update the prior information for the next frame. Using experiments on synthetic data, we evaluate the separation performance of the proposed algorithm. In addition, we apply the proposed algorithm to online video foreground and background separation from compressive measurements. The results show that the proposed method outperforms the existing methods.
Original languageEnglish
Title of host publicationProceedings of 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
Number of pages5
PublisherIEEE
Publication date2017
Pages1260-1264
ISBN (Electronic)978-1-5090-5990-4
DOIs
Publication statusPublished - 2017
Event5th IEEE Global Conference on Signal and Information Processing - Montreal, Canada
Duration: 14 Nov 201716 Nov 2017
Conference number: 5th

Conference

Conference5th IEEE Global Conference on Signal and Information Processing
Number5th
CountryCanada
CityMontreal
Period14/11/201716/11/2017

Keywords

  • Prior information
  • Robust PCA
  • n-1 minimization
  • Compressive measurements
  • Source separation

Cite this

Van Luong, H., Deligiannis, N., Seiler, J., Forchhammer, S., & Kaup, A. (2017). Compressive Online Robust Principal Component Analysis with Multiple Prior Information. In Proceedings of 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP) (pp. 1260-1264). IEEE. https://doi.org/10.1109/GlobalSIP.2017.8309163