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A novel SRC fusion method using hierarchical multi-scale LBP and greedy search strategy

机译：基于分层多尺度LBP和贪婪搜索策略的新型SRC融合方法

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摘要

As is well known, most of the sparse representation based classification (SRC) schemes are not the standard form of SR model, that is, the dictionary in SRC model is not a strict one compare to sparse representation model. Actually, a strict and standard dictionary will create sparser coefficient, but it might not achieve better classification accuracy in SRC model. Therefore, constructing a rational and optimal dictionary for SRC model is a challenging task for us. Understanding the good performance of such unconventional dictionaries demands new algorithmic and analytical techniques. In this paper, we propose a novel SRC fusion method using hierarchical multi-scale local binary patterns (LBP) and greedy search strategy (SRC-GSLBP) for face recognition. The proposed method involves three aspects: dictionary optimization, training variables selection and classification strategy, sparse coding coefficient decomposing. First of all, we get an optimized dictionary through extracting hierarchical multi-scale LBP features from the original training samples. Second, the training variables selection and classification strategy aim to represent a query sample as a linear combination of the most informative training samples, and exploits an optimal representation of training samples from the classes with major relevant contributions. Instead of eliminating several classes at one time, we choose eliminating classes one by one with greedy search (GS) sparse coding process until the predefined termination condition is satisfied. The final remaining training samples are used to produce a best representation of the test sample and to perform classification. In the context of the proposed method, an important goal is to select a subset of variables that accomplishes one objective: the provision of a descriptive representation for sparse category knowledge structure. We develop a heuristic learning strategy to achieve this goal. Experimental results conducted on the ORL, FERET and AR face databases demonstrate the effectiveness of the proposed method. (C) 2014 Elsevier B.V. All rights reserved.
机译：众所周知，大多数基于稀疏表示的分类（SRC）方案都不是SR模型的标准形式，也就是说，与稀疏表示模型相比，SRC模型中的字典不是严格的字典。实际上，严格而标准的字典会创建稀疏系数，但在SRC模型中可能无法获得更好的分类精度。因此，为我们建立一个合理，最优的SRC模型字典是一项艰巨的任务。了解此类非常规词典的良好性能需要新的算法和分析技术。在本文中，我们提出了一种新的SRC融合方法，该方法使用分层多尺度局部二进制模式（LBP）和贪婪搜索策略（SRC-GSLBP）进行人脸识别。该方法包括三个方面：字典优化，训练变量选择和分类策略，稀疏编码系数分解。首先，我们通过从原始训练样本中提取分层的多尺度LBP特征来获得优化的字典。其次，训练变量的选择和分类策略旨在将查询样本表示为信息量最大的训练样本的线性组合，并利用具有重大相关贡献的类别中训练样本的最佳表示形式。我们选择一次通过贪婪搜索（GS）稀疏编码过程逐个消除类，直到满足预定义的终止条件，而不是一次消除多个类。最后剩余的训练样本用于产生测试样本的最佳表示并执行分类。在提出的方法的上下文中，一个重要的目标是选择一个变量子集以实现一个目标：为稀疏类别知识结构提供描述性表示。我们制定了启发式学习策略来实现这一目标。在ORL，FERET和AR人脸数据库上进行的实验结果证明了该方法的有效性。 （C）2014 Elsevier B.V.保留所有权利。

著录项

  • 来源
    《Neurocomputing》 |2015年第3期|1455-1467|共13页
  • 作者

    Liu Zi; Song Xiaoning; Tang Zhenmin;

  • 作者单位

    Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Jiangsu, Peoples R China;

    Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Jiangsu, Peoples R China|Jiangnan Univ, Sch Internet Things Engn, Wuxi 214122, Peoples R China|Jiangsu Univ Sci & Technol, Sch Comp Sci & Engn, Zhenjiang 212003, Peoples R China;

    Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Jiangsu, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Sparse representation; Greedy search; Hierarchical multi-scale; Image classification;

    机译：稀疏表示;贪婪搜索;层次多尺度;图像分类;

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