Sparse representation for computer vision and pattern recognition

Journal Article

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. © 2010 IEEE.

Full Text

Duke Authors

Cited Authors

  • Wright, J; Ma, Y; Mairal, J; Sapiro, G; Huang, TS; Yan, S

Published Date

  • 2010

Published In

Volume / Issue

  • 98 / 6

Start / End Page

  • 1031 - 1044

International Standard Serial Number (ISSN)

  • 0018-9219

Digital Object Identifier (DOI)

  • 10.1109/JPROC.2010.2044470