Application of Locally Invariant Robust PCA for Underwater Image Recognition

Recently, many PCA with robust low-dimensional representation models have been applied in imaging. However, most models ignore the manifold geometry of the data and fail to minimize the reconstruction error. Here, a novel robust PCA structure, locally invariant robust principal component analysis (LIRPCA), is proposed for underwater image recognition. The contributions of LIRPCA are as follows: (1) LIRPCA selects the ℓ2-norm as distance metric criterion to describe the global geometry and the intrinsic geometry, which ensures the robustness of the overall model structure. (2) LIRPCA constructs a close relationship between the reconstruction error of the projected data and the input data in the cost function to minimize the reconstruction error. (3) To solve the challenging optimization function of LIRPCA, we design an iterative algorithm with fast convergence to obtain the desired solution. The proposed model is applied to feature extraction and recognition tasks from several underwater image datasets and performs better than other models.