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基于群稀疏的结构化字典学习
引用本文:郭景峰,李贤. 基于群稀疏的结构化字典学习[J]. 中国图象图形学报, 2012, 17(11): 1347-1352
作者姓名:郭景峰  李贤
作者单位:燕山大学信息科学与工程学院, 秦皇岛 066004;燕山大学信息科学与工程学院, 秦皇岛 066004
基金项目:河北省重点基础研究项目(10963527D)
摘    要:随着稀疏表示在机器学习和图像处理领域中的广泛应用,字典学习的算法受到越来越多的关注。传统意义上训练出来的字典只是一些原子的集合,没有结构。考虑到稀疏表示信号中群结构的稀疏性,建立了基于群稀疏的结构化字典学习的数学模型,并结合凸分析和单调算子理论提出了一个结构化字典学习的有效算法。实验结果表明,该算法具有更快的收敛速度,新模型训练出来的字典能够更好地适应数据,提高表示数据的精度,进而提高图像增强的效果。

关 键 词:稀疏表示  字典学习  群稀疏  凸优化  单调算子
收稿时间:2012-05-04
修稿时间:2012-06-02

Structured dictionary learning based on group sparsity
Guo Jingfeng and Li Xian. Structured dictionary learning based on group sparsity[J]. Journal of Image and Graphics, 2012, 17(11): 1347-1352
Authors:Guo Jingfeng and Li Xian
Affiliation:College of Information Science and engineering, Yanshan University, Qinhuangdao 066004, China;College of Information Science and engineering, Yanshan University, Qinhuangdao 066004, China
Abstract:Sparse representation of signals is an evolving field in many machine learning and image processing tasks. Nowadays, more and more attention is paid on the algorithm for learning dictionaries.Traditionally, the dictionary is an unstructured set of atoms. Considering the sparsity of the group of the sparse representation signal, a mathematical model of the dictionary learning based on the group sparsity is constructed. We propose an efficient algorithm for learning structured dictionary according to the convex analysis and monotone operator theory. The experiments show that the algorithm converges faster, the dictionary trained from the new model adapts better to the data and the data is better represented, which overall improves the image enhancement effect.
Keywords:sparse representation  dictionary learning  group sparsity  convex optimization  monotone operator
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