电子测量与仪器学报

陈璇,毕鹏飞,胡志远.任意三角形结构2DPCA在水下光学图像识别中的应用[J].电子测量与仪器学报,2024,38(12):43-53

任意三角形结构2DPCA在水下光学图像识别中的应用

Arbitrary triangle structure 2DPCA and its application tounderwater optical image recognition

DOI：

中文关键词: 二维主成分分析任意三角形结构鲁棒距离度量水下光学图像识别降维

英文关键词:two-dimensional principal component analysis (2DPCA) arbitrary triangle structure robust distance metric underwater optical image recognition dimensionality reduction

基金项目:江苏省基础研究计划基金(BK20220452)项目资助

作者	单位
陈璇	南京信息工程大学人工智能学院南京210044
毕鹏飞	南京信息工程大学人工智能学院南京210044
胡志远	南京信息工程大学人工智能学院南京210044

Author	Institution
Chen Xuan	School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China
Bi Pengfei	School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China
Hu Zhiyuan	School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China

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中文摘要:

在实际应用中,受观测条件和采集场景等诸多因素的综合作用,水下光学图像通常呈现出高维小样本的特性,与此同时,这类图像还极易伴随着各类噪声信息的干扰。导致许多降维方法在其识别过程中的鲁棒表现力不足。为解决上述问题,提出一种创新的任意三角形结构二维主成分分析方法(ATS-2DPCA)应用于水下光视觉图像识别。该方法在构建过程中,充分考虑了投影数据的重构误差和方差两者之间的关系,在此基础上成功匹配到了灵活的鲁棒距离度量机制。通过这种方式,能够切实有效地提升在面临噪声干扰时水下光学图像数据的识别精度,并且实现对于数据几何结构的合理保护。从理论层面证明了该方法的可用性和收敛性。同时,选取了3个水下光学图像数据库进行了实验验证,得出的最优识别精度分别为:89.07%、88.52%、86.00%。一系列实验结果有力地表明,ATS-2DPCA 在同类方法中展现出了更为卓越的性能表现。

英文摘要:

Influenced by factors such as observation conditions and acquisition scenarios, underwater optical image data usually presents the characteristics of high-dimensional small samples and is easily accompanied with noise interference. Resulting in insufficient robust performance of many dimensionality reduction methods in their recognition process. To solve this problem, we propose a novel 2DPCA method for underwater optical image recognition, called arbitrary triangle structure 2DPCA (ATS-2DPCA). On the basis of considering the relationship between reconstruction error and variance of projection data, ATS-2DPCA can successfully match the flexible robust distance metric mechanism, which effectively improves the accuracy of underwater optical image recognition under noise interference environment and achieves reasonable protection of the geometric structure of the data. In this paper, we theoretically prove the availability and convergence of the proposed method and use three underwater optical image databases for experimental verification. The optimal recognition accuracy is 89.07%, 88.52%, and 86.00%, respectively. The extensive experimental results show that ATS-2DPCA has more outstanding performance than other 2DPCA-based methods.

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