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Sparse Representation For Computer Vision and Pattern Recognition (2009)
| Citations: | 146 - 9 self |
BibTeX
@MISC{Wright09sparserepresentation,
author = {John Wright and Yi Ma and Julien Mairal and Guillermo Sapiro and Thomas Huang and Shuicheng Yan},
title = {Sparse Representation For Computer Vision and Pattern Recognition},
year = {2009}
}
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Abstract
Techniques from sparse signal representation are beginning to see significant impact in computer vision, often on non-traditional 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-theart 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.
Keyphrases
computer vision pattern recognition sparse representation unconventional dictionary sparse signal representation non-traditional application observed signal state-of-theart result analytical technique representative example dictionary play compact high-fidelity representation open question key role review paper highlight good performance semantic meaning turn demand semantic information significant impact signal representation
