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- Julien Mairal
- LEAR team, laboratoire Jean Kuntzmann, CNRS, Univ. Grenoble Alpes ,
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- Francis Bach
- SIERRA team, département d’informatique de l’Ecole Normale Sup’rieure, ENS/CNRS/Inria UMR 8548,
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- Jean Ponce
- WILLOW team, département d’informatique de l’Ecole Normale Supérieure, ENS/CNRS/Inria UMR 8548,
書誌事項
- 公開日
- 2014-12-19
- DOI
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- 10.1561/0600000058
- 公開者
- Emerald
この論文をさがす
説明
<jats:p>In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. In statistics and machine learning, the sparsity principle is used to perform model selection—that is, automatically selecting a simple model among a large collection of them. In signal processing, sparse coding consists of representing data with linear combinations of a few dictionary elements. Subsequently, the corresponding tools have been widely adopted by several scientific communities such as neuroscience, bioinformatics, or computer vision. The goal of this monograph is to offer a self-contained view of sparse modeling for visual recognition and image processing. More specifically, we focus on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.</jats:p>
収録刊行物
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- Foundations and Trends® in Computer Graphics and Vision
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Foundations and Trends® in Computer Graphics and Vision 8 (2-3), 85-283, 2014-12-19
Emerald
