Please use this identifier to cite or link to this item: https://doi.org/10.1109/JPROC.2010.2044470
Title: Sparse representation for computer vision and pattern recognition
Authors: Wright, J.
Ma, Y.
Mairal, J.
Sapiro, G.
Huang, T.S.
Yan, S. 
Keywords: Compressed sensing
Computer vision
Pattern recognition
Signal representations
Issue Date: Jun-2010
Source: Wright, J., Ma, Y., Mairal, J., Sapiro, G., Huang, T.S., Yan, S. (2010-06). Sparse representation for computer vision and pattern recognition. Proceedings of the IEEE 98 (6) : 1031-1044. ScholarBank@NUS Repository. https://doi.org/10.1109/JPROC.2010.2044470
Abstract: 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.
Source Title: Proceedings of the IEEE
URI: http://scholarbank.nus.edu.sg/handle/10635/57456
ISSN: 00189219
DOI: 10.1109/JPROC.2010.2044470
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