面向图像识别的测地局部典型相关分析方法

许欢; 苏树智; 颜文婧; 邓瀛灏; 谢军

doi:10.11999/JEIT200123

面向图像识别的测地局部典型相关分析方法

doi: 10.11999/JEIT200123

1.
安徽理工大学计算机科学与工程学院淮南 232001
2.
北京工商大学计算机与信息工程学院北京 100037

基金项目: 国家自然科学基金(61806006)，安徽省高等学校自然科学研究基金(KJ2018A0083)，中国博士后科学基金(2019M660149)

详细信息

作者简介:
许欢：女，1982年生，助教，研究方向为机器学习、图像处理、模式识别

苏树智：男，1987年生，副教授，研究方向为多模态模式识别、特征学习、子空间融合、图像处理

颜文婧：女，1984年生，讲师，研究方向为机器学习、模式识别、信号处理

通讯作者:
苏树智　sushuzhi@foxmail.com

中图分类号: TN911.73; TP391.4
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- 被引次数: 0
出版历程
- 收稿日期: 2020-02-21
- 修回日期: 2020-07-23
- 网络出版日期: 2020-07-23
- 刊出日期: 2020-11-16

A Geodesic Locality Canonical Correlation Analysis Method for Image Recognition

1.
College of Computer Science and Engineering, Anhui University of Science & Technology, Huainan 232001, China
2.
School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100037, China

Funds: The National Natural Science Foundation of China (61806006), The Anhui Province Natural Science Research Foundation of Institutions of Higher Learning (KJ2018A0083), The China Postdoctoral Science Foundation (2019M660149)

摘要

摘要: 典型相关分析(CCA)是一种经典的多模态特征学习方法，能够从不同模态同时学习相关性最大的低维特征，然而难以发现隐藏在样本空间中的非线性流形结构。该文提出一种基于测地流形的多模态特征学习方法，即测地局部典型相关分析(GeoLCCA)。该方法利用测地距离构建了低维相关特征的测地散布，并进一步通过最大化模态间的相关性和最小化模态内的测地散布学习更具鉴别力的非线性相关特征。该文不仅在理论上对提出的方法进行了分析，而且在真实的图像数据集上验证了方法的有效性。
- 图像识别 /
- 典型相关分析 /
- 多模态特征学习 /
- 流形学习
Abstract: Canonical Correlation Analysis (CCA) is a classic multi-modal feature learning method, which can learn low-dimensional features with the maximum correlation from different modalities. However, it is difficult for CCA to find the nonlinear manifold structures hidden in the sample spaces. This paper proposes a multi-modal feature learning method based on geodesic manifolds, namely Geodesic Locality Canonical Correlation Analysis (GeoLCCA).The geodesic distances are used to construct the geodesic scatters of low-dimensional correlation features, and the nonlinear correlation features with better discriminative power are learned by maximizing the between-modal correlation and minimizing the within-modal geodesic scatters. This paper not only analyzes the proposed method in theory, but also verifies the effective of the proposed method on the real-world image datasets.
- Image recognition /
- Canonical Correlation Analysis (CCA) /
- Multi-modal feature learning /
- Manifold learning

HTML全文

表 1 在GT图像数据集上的识别率(%)及标准差

	训练样本数5	训练样本数6	训练样本数7	训练样本数8
GeoLCCA	67.26±2.01	71.36±1.83	76.10±1.28	78.20±1.31
GMCCA	65.22±1.64	66.64±1.56	69.70±1.75	72.06±1.66
LPCCA	44.84±1.73	50.09±3.79	54.15±1.74	57.46±2.56
DMCCA	63.56±2.77	67.80±1.29	73.67±1.71	75.80±1.99
CCA	59.08±1.81	61.78±1.35	66.22±1.66	68.14±2.01
A±B: A表示平均识别率(%)，B表示对应的识别率标准差

下载: 导出CSV

表 2 在ORL图像数据集上的识别率(%)及标准差

	训练样本数5	训练样本数6	训练样本数7	训练样本数8
GeoLCCA	95.15±1.58	97.19±1.33	98.25±0.83	99.50±0.65
GMCCA	93.90±2.04	95.19±0.89	97.00±1.53	98.50±1.42
LPCCA	84.70±3.00	87.81±2.40	89.17±2.00	94.25±2.58
DMCCA	93.80±1.53	95.50±1.74	96.75±1.49	99.38±0.66
CCA	90.35±1.94	93.19±1.94	93.83±1.68	97.25±1.15
A±B: A表示平均识别率(%)，B表示对应的识别率标准差

下载: 导出CSV

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