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

BI Pengfei; HU Zhiyuan; CHEN Xuan; DU Xue

doi:10.11999/JEIT240359

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BI Pengfei, HU Zhiyuan, CHEN Xuan, DU Xue. Underwater Optical Image Recognition Based on Dual Flexible Metric Adaptive Weighted 2DPCA[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240359

Citation:

BI Pengfei, HU Zhiyuan, CHEN Xuan, DU Xue. Underwater Optical Image Recognition Based on Dual Flexible Metric Adaptive Weighted 2DPCA[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240359

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

doi: 10.11999/JEIT240359

BI Pengfei^{1, 2
,
,},
HU Zhiyuan¹,
CHEN Xuan¹,
DU Xue³

1.
School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.
Hubei Key Laboratory of Inland Shipping Technology, Wuhan University of Technology, Wuhan 430063, China
3.
School of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150006, China

Funds: The National Natural Science Foundation of China (5217110032), The Natural Science Basic Research Plan in Jiangsu Province of China (BK20220452), The Hubei Key Laboratory of Inland Shipping Technology (NHHY2022004), The Jiangsu Province Graduate Student Practice Innovation Program Project (SJCX24_0479)

Received Date: 2024-05-07
Rev Recd Date: 2024-09-02

Available Online: 2024-09-09

Abstract

Abstract

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.
- Pattern recognition,
- Robust distance metric,
- Adaptive weighted,
- Underwater optical image,
- Two-dimensional principal component analysis

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References(22)

References

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