Sparse Modeling for Image and Vision Processing

Julien Mairal 1 Francis Bach 2, 3 Jean Ponce 4, 2
1 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
3 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique de l'École normale supérieure, ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
4 WILLOW - Models of visual object recognition and scene understanding
DI-ENS - Département d'informatique de l'École normale supérieure, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
Abstract : 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.
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Contributor : Julien Mairal <>
Submitted on : Saturday, December 6, 2014 - 2:57:23 PM
Last modification on : Tuesday, June 18, 2019 - 3:14:07 PM
Long-term archiving on : Monday, March 9, 2015 - 6:07:22 AM

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Julien Mairal, Francis Bach, Jean Ponce. Sparse Modeling for Image and Vision Processing. now publishers, 8 (2-3), pp.85-283, 2014, Foundations and Trends in Computer Graphics and Vision, 978-1-68083-008-8. ⟨10.1561/0600000058⟩. ⟨http://www.nowpublishers.com/⟩. ⟨hal-01081139v2⟩

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