Kernel Transfer Sparse Coding Algorithms Application for Cross-domain Image Classification

SUN Deng-di; MENG Qian-qian; MA Yun-peng

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Kernel Transfer Sparse Coding Algorithms Application for Cross-domain Image Classification

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- Kernel Transfer Sparse Coding Algorithms Application for Cross-domain Image Classification
- Vol. 35, Issue 10, Pages: 29-35(2018)
- 作者机构：
  
  1. 安徽大学计算机与科学与技术学院,安徽,合肥,230601
  2. 安徽大学计算智能与信号处理重点实验室, 安徽合肥 2300601
- 作者简介：
- 基金信息：
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- Published：2018
- 稿件说明：
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孙登第, 孟欠欠, 马云鹏. 核迁移稀疏编码算法在跨域图像分类中的应用[J]. 微电子学与计算机, 2018, 35(10): 29-35.

SUN Deng-di, MENG Qian-qian, MA Yun-peng. Kernel Transfer Sparse Coding Algorithms Application for Cross-domain Image Classification[J]. 2018, 35(10): 29-35.
孙登第, 孟欠欠, 马云鹏. 核迁移稀疏编码算法在跨域图像分类中的应用[J]. 微电子学与计算机, 2018, 35(10): 29-35. DOI：

SUN Deng-di, MENG Qian-qian, MA Yun-peng. Kernel Transfer Sparse Coding Algorithms Application for Cross-domain Image Classification[J]. 2018, 35(10): 29-35. DOI：

摘要

针对非线性分布的数据样本在原始特征空间可分性较差的问题

文中提出一种基于核迁移稀疏编码的跨域图像分类方法

并应用于图像分类.首先将图像特征和字典映射到一个高维的再生核希尔伯特空间

使得线性不可分问题变为线性可分问题.然后在高维特征空间中对每个样本数据进行表示.文中算法不仅有效地处理非线性结构数据

而且考虑了源域和目标域的分布差异以及几何结构信息

获得更为鲁棒的稀疏表达

提高跨域图像分类精度.

Abstract

To deal with the problem that the data samples with non-linear distribution are poorly separable in the original feature space

in this paper

a kernel transfer sparse soding algorithms is proposed for cross-domain image classification.Firstly

the image features and the dictionary are mapped to a high-dimensional reproducing-kernel hilbert space

which makes the problem of linear inseparable problem become a linear separable problem. Then

the samples are individually represented in high-dimensional feature space.The proposed algorithm not only effectively handles the the nonlinear structure data

but also considers the distribution differences and geometric structure information between source and target domains

which gain more robust sparse representation and improve cross-domain image classification accuracy.

关键词

Keywords

references

Long M. Transfer sparse coding for robust image representation[J]. Computer Vision % Pattern Recognition, 2013, 9 (4) :407-414. DOI: 407

Long M, Wang J, Ding G, et al. Transfer Feature Learning with Joint Distribution Adaptation[C]// IEEE International Conference on Computer Vision. IEEE, 2014: 2200-2207

尚丽.稀疏编码算法及其应用研究[D].合肥: 中国科学技术大学, 2006.

谭暑秋, 陈东义, 郭承刚, 等.一种基于稀疏编码的人脸特征点检测方法[J].微电子学与计算机, 2017, 35(9):7-10. DOI: 7

迪可新, 魏平, 柯钧, 等.基于压缩感知的空间稀疏目标成像方法研究[J].微电子学与计算机, 2015, 33(10):98-104. DOI: 98

刘涛.基于核拉普拉斯稀疏编码的图像分类[D].大连: 大连理工大学, 2013.

赵鹏, 王维.基于迁移鲁棒稀疏编码的图像表示方法[J].计算机学报, 2017, 39(10):2421-2432. DOI: 2421

姜海燕, 刘昊天, 舒欣, 等.基于最大均值差异的多标记迁移学习算法[J].信息与控制, 2016, 45(4):463-470. DOI: 463

Zheng M. Graph regularized sparse coding for image representation[J]. IEEE Transactions on Image Processing, 2011, 20(5): 1327-1336. DOI: 1327

王铁建, 吴飞, 荆晓远.核字典学习在软件缺陷预测中的应用[J].小型微型计算机系统, 2017, 38(7):1501-1505. DOI: 1501

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