Sparse Representation for Computer Vision and Pattern Recognition

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 di...

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Vydáno v:	Proceedings of the IEEE Ročník 98; číslo 6; s. 1031 - 1044
Hlavní autoři:	Wright, John, Ma, Yi, Mairal, Julien, Sapiro, Guillermo, Huang, Thomas S., Yan, Shuicheng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.06.2010 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithm design and analysis Algorithms Application software Asia Compressed sensing Computer vision Data mining Dictionaries Face recognition Mathematical analysis Pattern recognition Representations Semantics Signal processing algorithms Signal representations State of the art
ISSN:	0018-9219, 1558-2256
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

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.
AbstractList	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.
Author	Yan, Shuicheng Ma, Yi Sapiro, Guillermo Mairal, Julien Wright, John Huang, Thomas S.
Author_xml	– sequence: 1 givenname: John surname: Wright fullname: Wright, John email: jnwright@uiuc.edu organization: Visual Computing Group, Microsoft Research Asia, Beijing, China – sequence: 2 givenname: Yi surname: Ma fullname: Ma, Yi email: yima@uiuc.edu organization: Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana – sequence: 3 givenname: Julien surname: Mairal fullname: Mairal, Julien email: julien.mairal@m4x.org organization: Laboratoire d'Informatique de l'Ecole Normale Supérieure (INRIA/ENS/CNRS UMR 8548), INRIA-Willow project, Ecole Normale Supérieure, Paris, France – sequence: 4 givenname: Guillermo surname: Sapiro fullname: Sapiro, Guillermo email: guille@ece.umn.edu organization: Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis – sequence: 5 givenname: Thomas S. surname: Huang fullname: Huang, Thomas S. email: t-huang1@illinois.edu organization: Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign , Urbana – sequence: 6 givenname: Shuicheng surname: Yan fullname: Yan, Shuicheng email: eleyans@nus.edu.sg organization: Department of Electrical and Computer Engineering, National University of Singapore, Singapore
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Snippet	Techniques from sparse signal representation are beginning to see significant impact in computer vision, often on nontraditional applications where the goal is...
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SubjectTerms	Algorithm design and analysis Algorithms Application software Asia Compressed sensing Computer vision Data mining Dictionaries Face recognition Mathematical analysis Pattern recognition Representations Semantics Signal processing algorithms Signal representations State of the art
Title	Sparse Representation for Computer Vision and Pattern Recognition
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