Sparse representation for computer vision and pattern recognition

John Wright, Yi Ma, Julien Mairal, Guillermo Sapiro, Thomas S. Huang, Shuicheng Yan

Research output: Contribution to journalArticlepeer-review


Techniques from sparse signal representation are beginning to see significant impact in computer vision, often on nontraditional applications where the goal is not just to obtain a compact high-fidelity representation of the observed signal, but also to extract semantic information. The choice of dictionary plays a key role in bridging this gap: unconventional dictionaries consisting of, or learned from, the training samples themselves provide the key to obtaining state-of-the-art results and to attaching semantic meaning to sparse signal representations. Understanding the good performance of such unconventional dictionaries in turn demands new algorithmic and analytical techniques. This review paper highlights a few representative examples of how the interaction between sparse signal representation and computer vision can enrich both fields, and raises a number of open questions for further study.

Original languageEnglish (US)
Article number5456194
Pages (from-to)1031-1044
Number of pages14
JournalProceedings of the IEEE
Issue number6
StatePublished - Jun 2010


  • Compressed sensing
  • Computer vision
  • Pattern recognition
  • Signal representations

ASJC Scopus subject areas

  • General Computer Science
  • Electrical and Electronic Engineering


Dive into the research topics of 'Sparse representation for computer vision and pattern recognition'. Together they form a unique fingerprint.

Cite this