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非负局部约束线性编码图像分类算法

刘培娜 刘国军 郭茂祖 刘扬 李盼

刘培娜, 刘国军, 郭茂祖, 刘扬, 李盼. 非负局部约束线性编码图像分类算法. 自动化学报, 2015, 41(7): 1235-1243. doi: 10.16383/j.aas.2015.c140753
引用本文: 刘培娜, 刘国军, 郭茂祖, 刘扬, 李盼. 非负局部约束线性编码图像分类算法. 自动化学报, 2015, 41(7): 1235-1243. doi: 10.16383/j.aas.2015.c140753
LIU Pei-Na, LIU Guo-Jun, GUO Mao-Zu, LIU Yang, LI Pan. Image Classification Based on Non-negative Locality-constrained Linear Coding. ACTA AUTOMATICA SINICA, 2015, 41(7): 1235-1243. doi: 10.16383/j.aas.2015.c140753
Citation: LIU Pei-Na, LIU Guo-Jun, GUO Mao-Zu, LIU Yang, LI Pan. Image Classification Based on Non-negative Locality-constrained Linear Coding. ACTA AUTOMATICA SINICA, 2015, 41(7): 1235-1243. doi: 10.16383/j.aas.2015.c140753

非负局部约束线性编码图像分类算法

doi: 10.16383/j.aas.2015.c140753
基金项目: 

国家自然科学基金 (61171185, 61271346), 黑龙江省青年科学基金 (QC2014C071)资助

详细信息
    作者简介:

    刘培娜哈尔滨工业大学计算机科学与技术学院硕士研究生. 2013 年获得内蒙古大学计算机学院学士学位. 主要研究方向为计算机视觉与机器学习.E-mail: liupeina@hit.edu.cn

Image Classification Based on Non-negative Locality-constrained Linear Coding

Funds: 

Supported by National Natural Science Foundation of China (61171185, 61271346) and Heilongjiang Province Science Foundation for Youths (QC2014C071)

  • 摘要: 基于特征提取的图像分类算法的核心问题是如何对特征进行有效编码. 局部约束线性编码(Locality-constrained linear coding, LLC) 因其良好的特征重构性与局部平滑稀疏性, 已取得了很好的分类性能. 然而, LLC编码的分类性能对编码过程中的近邻数k的大小比较敏感, 随着k的增大, 编码中的某些负值元素与正值元素的差值绝对值也可能增大, 这使得LLC越来越不稳定. 本文通过在LLC优化模型的目标方程中引入非负约束, 提出了一种新型编码方式, 称为非负局部约束线性编码(Non-negative locality-constrained linear coding, NNLLC). 该模型一般采取迭代优化算法进行求解, 但其计算复杂度较大. 因此, 本文提出两种近似非负编码算法, 其编码速度与LLC一样快速. 实验结果表明, 在多个广泛使用的图像数据集上, 相比于LLC, NNLLC编码方式不仅在分类精确率上提高了近1%~4%, 而且对k的选取具有更强的鲁棒性.
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出版历程
  • 收稿日期:  2014-10-28
  • 修回日期:  2015-03-03
  • 刊出日期:  2015-07-20

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