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

BI Pengfei; HU Zhiyuan; CHEN Xuan; DU Xue

doi:10.11999/JEIT240359

Volume 46 Issue 11

Nov. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(11): 4188-4197

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, 2024, 46(11): 4188-4197. 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, 2024, 46(11): 4188-4197. 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), Hubei Key Laboratory of Inland Shipping Technology (NHHY2022004), 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

Publish Date: 2024-11-10

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

[1]	ZHOU Jingchun, LIU Qian, JIANG Qiuping, et al. Underwater camera: Improving visual perception via adaptive dark pixel prior and color correction[J]. International Journal of Computer Vision, 2023, 72(2): 1574–1585. doi: 10.1007/s11263-023-01853-3.
[2]	郭银景, 吴琪, 苑娇娇, 等. 水下光学图像处理研究进展[J]. 电子与信息学报, 2021, 43(2): 426–435. doi: 10.11999/JEIT190803. GUO Yinjing, WU Qi, YUAN Jiaojiao, et al. Research progress on underwater optical image processing[J]. Journal of Electronics & Information Technology, 2021, 43(2): 426–435. doi: 10.11999/JEIT190803.
[3]	JOLLIFFE I T and CADIMA J. Principal component analysis: A review and recent developments[J]. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2016, 374(2065): 20150202. doi: 10.1098/rsta.2015.0202.
[4]	MARTINEZ A M and KAK A C. PCA versus LDA[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(2): 228–233. doi: 10.1109/34.908974.
[5]	HE Xiaofei, YAN Shuicheng, HU Yuxiao, et al. Face recognition using Laplacianfaces[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(3): 328–340. doi: 10.1109/TPAMI.2005.55.
[6]	YANG Jian, ZHANG D, FRANGI A F, et al. Two-dimensional PCA: A new approach to appearance-based face representation and recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(1): 131–137. doi: 10.1109/TPAMI.2004.1261097.
[7]	LI Ming and YUAN Baozong. 2D-LDA: A statistical linear discriminant analysis for image matrix[J]. Pattern Recognition Letters, 2005, 26(5): 527–532. doi: 10.1016/j.patrec.2004.09.007.
[8]	CHEN Sibao, ZHAO Haifeng, KONG Min, et al. 2D-LPP: A two-dimensional extension of locality preserving projections[J]. Neurocomputing, 2007, 70(4/6): 912–921. doi: 10.1016/j.neucom.2006.10.032.
[9]	WANG Rong, NIE Feiping, YANG Xiaojun, et al. Robust 2DPCA with non-greedy l₁-norm maximization for image analysis[J]. IEEE Transactions on Cybernetics, 2015, 45(5): 1108–1112. doi: 10.1109/TCYB.2014.2341575.
[10]	WANG Haixian and WANG Jing. 2DPCA with L1-norm for simultaneously robust and sparse modelling[J]. Neural Networks, 2013, 46: 190–198. doi: 10.1016/j.neunet.2013.06.002.
[11]	WANG Jing. Generalized 2-D principal component analysis by Lp-norm for image analysis[J]. IEEE Transactions on Cybernetics, 2016, 46(3): 792–803. doi: 10.1109/TCYB.2015.2416274.
[12]	王肖锋, 陆程昊, 郦金祥, 等. 广义余弦二维主成分分析[J]. 自动化学报, 2022, 48(11): 2836–2851. doi: 10.16383/j.aas.c190392. WANG Xiaofeng, LU Chenghao, LI Jinxiang, et al. Generalized cosine two-dimensional principal component analysis[J]. Acta Automatica Sinica, 2022, 48(11): 2836–2851. doi: 10.16383/j.aas.c190392.
[13]	LI Tao, LI Mengyuan, GAO Quanxue, et al. F-norm distance metric based robust 2DPCA and face recognition[J]. Neural Networks, 2017, 94: 204–211. doi: 10.1016/j.neunet.2017.07.011.
[14]	GAO Quanxue, XU Sai, CHEN Fang, et al. R₁-2-DPCA and face recognition[J]. IEEE Transactions on Cybernetics, 2019, 49(4): 1212–1223. doi: 10.1109/TCYB.2018.2796642.
[15]	ZHAO Meixiang, JIA Zhigang, CAI Yunfeng, et al. Advanced variations of two-dimensional principal component analysis for face recognition[J]. Neurocomputing, 2021, 452: 653–664. doi: 10.1016/j.neucom.2020.08.083.
[16]	ZHOU Gongyu, XU Guangquan, HAO Jianye, et al. Generalized centered 2-D principal component analysis[J]. IEEE Transactions on Cybernetics, 2021, 51(3): 1666–1677. doi: 10.1109/TCYB.2019.2931957.
[17]	GAO Quanxue, MA Lan, LIU Yang, et al. Angle 2DPCA: A new formulation for 2DPCA[J]. IEEE Transactions on Cybernetics, 2018, 48(5): 1672–1678. doi: 10.1109/TCYB.2017.2712740.
[18]	WANG Xiaofeng, SHI Leyan, LIU Jun, et al. Cosine 2DPCA with weighted projection maximization[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(12): 9643–9656. doi: 10.1109/TNNLS.2022.3159011.
[19]	ZHANG Huanxing, BI Hongxu, WANG Xiaofeng, et al. A joint-norm distance metric 2DPCA for robust dimensionality reduction[J]. Information Sciences, 2023, 640: 119036. doi: 10.1016/j.ins.2023.119036.
[20]	HOLMES T H, WILSON S K, VANDERKLIFT M, et al. The role of Thalassoma lunare as a predator of juvenile fish on a sub-tropical coral reef[J]. Coral Reefs, 2012, 31(4): 1113–1123. doi: 10.1007/s00338-012-0934-8.
[21]	JIA Tianlong, KAPELAN Z, DE VRIES R, et al. Deep learning for detecting macroplastic litter in water bodies: A review[J]. Water Research, 2023, 231: 119632. doi: 10.1016/j.watres.2023.119632.
[22]	XU Jian, BI Pengfei, DU Xue, et al. Robust PCANet on target recognition via the UUV optical vision system[J]. Optik, 2019, 181: 588–597. doi: 10.1016/j.ijleo.2018.12.098.