Underwater Optical Image Recognition Based on Dual Flexible Metric Adaptive Weighted 2DPCA
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 recognitionRobust distance metricAdaptive weightedUnderwater optical imageTwo-dimensional principal component analysis
万方数据
维普
