基于图像欧氏距离的二维局部多样性保持投影

高全学; 高菲菲; 郝秀娟; 程洁

doi:10.3724/SP.J.1004.2013.01062

基于图像欧氏距离的二维局部多样性保持投影

doi: 10.3724/SP.J.1004.2013.01062

1.
西安电子科技大学ISN国家重点实验室西安 710071

基金项目:

国家自然科学基金(61271296, 60802075), 陕西省自然科学基础研究计划(2012JM8002), 浙江大学CAD & CG国家重点实验室开放课题(A1106), 中国博士后基金(2012M521747), 高等学校学科创新引智计划(B08038), 中央基本科研业务费,西安电子科技大学ISN国家重点实验室自主研究课题资助

详细信息

通讯作者:
高全学

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出版历程
- 收稿日期: 2012-06-07
- 修回日期: 2013-01-06
- 刊出日期: 2013-07-20

Image Euclidean Distance-based Two-dimensional Local Diversity Preserving Projection

1.
The State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an 710071

Funds:

Supported by National Natural Science Foundation of China (61271296, 60802075), Natural Science Basic Research Plan in Shaanxi Province of China (2012JM8002), the Open Project Program of the State Key Laboratory of CAD & CG, Zhejiang University (A1106), China Postdoctoral Science Foundation (2012M521747), 111 Project of China (B08038), Fundamental Research Funds for the Central Universities of China, and the State Key Laboratory of Integrated Service Networks, Xidian University

摘要

摘要: 主成分分析可以较好地保持数据的全局多样性几何属性, 在模式识别、机器学习、图像识别等领域有着很重要的作用. 缺点是他不能较好地保持局部数据的多样性几何属性, 且忽略了图像像素之间的相互关系, 导致算法性能不够好, 且对模式形变比较敏感. 对此问题, 提出了一种基于图像欧氏距离的二维局部多样性保持投影. 该方法利用邻接图描述局部数据之间的变化关系, 然后利用图像欧氏距离度量数据间的多样性几何属性, 有效地将图像像素之间的相互关系嵌入到目标函数中. 和主成分分析相比, 所提方法较好地保持了局部数据的多样性几何属性, 而且明确考虑了图像像素之间的相互关系, 对模式形变具有好的鲁棒性. 在Yale, AR及PIE三个人脸库上的实验结果证明了所提算法的有效性.
- 二维主成分分析 /
- 多样性 /
- 流形学习 /
- 特征提取 /
- 人脸识别
Abstract: Previous works have demonstrated that principal component analysis (PCA) well preserves the global information, i.e., diversity of data, and plays an important role in pattern recognition, machine learning, and image processing. However, PCA ignores the spatial relationships among pixels in images and does not well preserve the local diversity of data, which will impair the recognition accuracy and lead to unstableness to the perturbation of images. To address these problems, a novel approach, namely image Euclidean distance based two-dimensional local diversity preserving projection (IED-2DLDPP) is proposed. IED-2DLDPP constructs an adjacency graph to model the variation of data and employs image Euclidean distance to characterize the diversity of data, which explicitly considers the spatial relationships among pixels in the images. Extensive experiments on Yale, AR, and PIE databases show the efficiency of the proposed method.
- Two-dimensional principal component analysis (2DPCA) /
- diversity /
- manifold learning /
- feature extraction /
- face recognition

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