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
Key words
Pattern recognition/Robust distance metric/Adaptive weighted/Underwater optical image/Two-dimensional principal component analysis
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