Correntropy self-weighted based joint regularized nearest points for images set classification algorithm

doi:10.11772/j.issn.1001-9081.2019030463

Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (9): 2547-2551.DOI: 10.11772/j.issn.1001-9081.2019030463

• Artificial intelligence • Previous Articles Next Articles

Correntropy self-weighted based joint regularized nearest points for images set classification algorithm

REN Zhenwen<sup>1,2</sup>, WU Mingna<sup>1</sup>

1. School of National Defence Science and Technology, Southwest University of Science and Technology, Mianyang Sichuan 621010, China;
2. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing Jiangsu 210094, China

Received:2019-03-20 Revised:2019-04-22 Online:2019-09-10 Published:2019-05-08
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61673220), the Project of State Administration of Science, Technology and Industry for National Defense (JCKY2017209B010, JCKY2018209B001), the Project of Sichuan Office of Science, Technology and Industry for National Defense (ZYF-2018-106), the Innovation Foundation for College Students of Southwest University of Science and Technology (cx18-029).

基于熵自加权联合正则化最近点的图像集分类算法

任珍文^1,2, 吴明娜¹

1. 西南科技大学国防科技学院, 四川绵阳 621010;
2. 南京理工大学计算机科学与工程学院, 南京 210094

通讯作者: 任珍文
作者简介:任珍文(1987-),男,四川南充人,讲师,博士,主要研究方向:机器学习、计算机视觉、压缩感知;吴明娜(1998-),女,安徽宿州人,主要研究方向:机器学习、计算机视觉。
基金资助:
国家自然科学基金资助项目（61673220）；国家国防科技工业局项目（JCKY2017209B010，JCKY2018209B001）；四川省省工办项目（ZYF-2018-106）；西南科技大学大学生创新基金资助项目（cx18-029）。

Abstract

Abstract:

Image set classification algorithms, which make full use of the image set information to improve the recognition performance, have gained much attention. However, existing image set classification algorithms have the following problems:1) samples need to obey a certain probability and statistical distribution; 2) ignoring the mutual exclusion between classes in the gallery set; 3) without robustness against non-Gaussian noise. In order to solve the above problems, an image set classification algorithm based on Correntropy Self-weighted based joint Regularization of Nearest Points (SRNPC) was proposed. Firstly, the unique global joint regularization nearest point in the test set was found and the distance between this point and the regularization nearest point in each gallery set was minimized simultaneously. Then, to enhance the discrimination between classes and the robustness against non-Gaussian noise, a self-weighting strategy based on correntropy scale was introduced to update the correntropy weight between the test set and each gallery set iteratively. And the obtained weight was able to directly reflect the correlation between the test set and each gallery set. Finally, the classification result was obtained by using the minimum residual value between the test set and each gallery set. Experimental results on three open datasets UCSD/Honda, CMU Mobo and YouTube show that SRNPC has higher classification accuracy and better robustness than many state-of-the-art image classification algorithms.

Key words: image set classification, regularized nearest point, correntropy, face recognition, pattern recognition

摘要：

图像集分类算法通过充分利用图像的集合信息来提高识别性能，得到了广泛的关注。但是现有的图像集分类算法存在如下问题：1）需要样本满足某种概率统计分布；2）忽略了图库集类与类之间的互斥性；3）对非高斯噪声不具备鲁棒性。为了解决上述问题，提出了一种基于熵自加权联合正则化最近点的图像集分类算法（SRNPC）。首先在测试集中寻找唯一的全局联合正则化最近点，同时最小化该点与每个图库集中正则化最近点之间的距离；然后，为了增强类之间的判别力以及对非高斯噪声的鲁棒性，引入一种基于熵尺度的自加权策略来迭代更新测试集与各个图库集合之间的熵加权权重，得到的权重能够直接反映测试集与每个图库集之间相关性的高低；最后，利用测试集和每个图库集之间的最小残差值获得分类结果。通过在UCSD/Honda、CMU Mobo和YouTube这三个公开数据集上与当前主流的算法进行的对比实验结果表明，所提出的算法具有更高的分类精度和更强的鲁棒性。

关键词: 图像集分类, 正则化最近点, 相对熵, 人脸识别, 模式识别

CLC Number:

TP391.4

REN Zhenwen, WU Mingna. Correntropy self-weighted based joint regularized nearest points for images set classification algorithm[J]. Journal of Computer Applications, 2019, 39(9): 2547-2551.

任珍文, 吴明娜. 基于熵自加权联合正则化最近点的图像集分类算法[J]. 计算机应用, 2019, 39(9): 2547-2551.

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Correntropy self-weighted based joint regularized nearest points for images set classification algorithm

基于熵自加权联合正则化最近点的图像集分类算法

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