双灵活度量自适应加权2DPCA在水下光学图像识别中的应用  

Underwater Optical Image Recognition Based on Dual Flexible Metric Adaptive Weighted 2DPCA

作　　者：毕鹏飞 胡志远 陈璇[1] 杜雪[3] BI Pengfei;HU Zhiyuan;CHEN Xuan;DU Xue(School of Artificial Intelligence,Nanjing University of Information Science and Technology,Nanjing 210044,China;Hubei Key Laboratory of Inland Shipping Technology,Wuhan University of Technology,Wuhan 430063,China;School of Intelligent Systems Science and Engineering,Harbin Engineering University,Harbin 150006,China)

机构地区：[1]南京信息工程大学人工智能学院,南京210044 [2]内河航运技术湖北省重点实验室,武汉430063 [3]哈尔滨工程大学智能科学与工程学院,哈尔滨150006

出　　处：《电子与信息学报》2024年第11期4188-4197,共10页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(5217110032);江苏省基础研究计划基金(BK20220452);内河航运技术湖北省重点实验室基金(NHHY2022004);江苏省研究生实践创新计划项目(SJCX24_0479)。

摘　　要：受观测条件和采集场景等因素影响,水下光学图像通常呈现出高维小样本特性且易伴随着噪声信息干扰,导致许多降维方法对其识别过程中的鲁棒表现力不足。为解决上述问题,该文提出一种新颖的双灵活度量自适应加权2维主成分分析方法(DFMAW-2DPCA)应用于水下图像识别。该方法不仅在建立重构误差和方差之间双层关系中同时使用了灵活的鲁棒距离度量机制,而且能够根据每个样本实际状态自适应学习到与之相匹配的权重,有效增强了模型在水下噪声干扰环境下的鲁棒性并实现识别精度的提升。与此同时,该文设计了一个快速非贪婪算法用于最优解的获取,其具有良好的收敛性。通过3个水下图像数据库中进行大量实验的结果表明,DFMAW-2DPCA在同类方法中具有更为杰出的整体性能。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 many dimension reduction methods lacking robust performance in their recognition process.To solve this problem,a novel 2DPCA method for underwater image recognition,called Dual Flexible Metric Adaptive Weighted 2DPCA(DFMAW-2DPCA),is proposed.DFMAW-2DPCA not only utilizes a flexible robust distance metric mechanism in establishing a dual-layer relationship between reconstruction error and variance,but also adaptively learn matching weights based on the actual state of each sample,which effectively enhances the robustness of the model in underwater noise interference environments and improves recognition accuracy.In this paper,a fast nongreedy algorithm for obtaining the optimal solution is designed and has good convergence.The extensive experimental results on three underwater image databases show that DFMAW-2DPCA has more outstanding overall performance than other 2DPCA-based methods.

关 键 词：模式识别 鲁棒距离度量 自适应加权 水下光学图像 2维主成分分析 

分 类 号：TN911.73[电子电信—通信与信息系统] TP391.41[电子电信—信息与通信工程]