Sparse representation and learning in visual recognition: Theory and applications
Sparse representation and learning in visual recognition: Theory and applications
复制标题
视觉识别中的稀疏表示和学习：理论与应用
DOI：
10.1016/j.sigpro.2012.09.011
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发表时间：
2013-06-01
影响因子：
4.4
通讯作者：
Chen, Xuewen
中科院分区：
文献类型：
--
作者：
Cheng, Hong;Liu, Zicheng;Chen, Xuewen
稀疏表示和学习在计算智能、机器学习、计算机视觉和模式识别等领域有着广泛的应用。从数学上讲，解决稀疏表示和学习问题涉及到从过完备字典中寻找基函数的稀疏线性组合。这背后的原因是人脑中节点之间的稀疏连接。本文介绍了最近的一些工作稀疏表示，学习和建模，重点是视觉识别。它涵盖了理论和应用两个方面。首先回顾了稀疏表示和学习理论，包括一般稀疏表示、结构化稀疏表示、高维非线性学习、贝叶斯压缩感知、稀疏子空间学习、非负稀疏表示、鲁棒稀疏表示和高效稀疏表示。然后，我们将介绍稀疏理论在各种视觉识别任务中的应用，包括特征表示和选择、字典学习、稀疏诱导相似性（SIS）度量、基于稀疏编码的分类框架以及稀疏相关主题。(c)2012爱思唯尔有限公司版权所有。
Sparse representation and learning has been widely used in computational intelligence, machine learning, computer vision and pattern recognition, etc. Mathematically, solving sparse representation and learning involves seeking the sparsest linear combination of basis functions from an overcomplete dictionary. A rational behind this is the sparse connectivity between nodes in human brain. This paper presents a survey of some recent work on sparse representation, learning and modeling with emphasis on visual recognition. It covers both the theory and application aspects. We first review the sparse representation and learning theory including general sparse representation, structured sparse representation, high-dimensional nonlinear learning, Bayesian compressed sensing, sparse subspace learning, non-negative sparse representation, robust sparse representation, and efficient sparse representation. We then introduce the applications of sparse theory to various visual recognition tasks, including feature representation and selection, dictionary learning, Sparsity Induced Similarity (SIS) measures, sparse coding based classification frameworks, and sparsity-related topics. (c) 2012 Elsevier B.V. All rights reserved.