Year: 2014
Type: article
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.
DOI: 10.1561/0600000058
Authors: Julien Mairal, Francis Bach, Jean Ponce
Institutions: Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, Laboratoire Jean Kuntzmann, Centre Inria de Paris, Université Grenoble Alpes, École Normale Supérieure - PSL, Département d'Informatique, SIERRA: Apprentissage Statistique et optimisation, WILLOW: Vision par ordinateur incarnée
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2949902569