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Books Year : 2014

Sparse Modeling for Image and Vision Processing

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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Dates and versions

hal-01081139 , version 1 (07-11-2014)
hal-01081139 , version 2 (06-12-2014)

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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⟩. ⟨hal-01081139v2⟩
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