Search

In this paper, we propose a novel Schatten p -norm-based two-dimensional principal component analysis (2DPCA) method, which is named after 2DPCA-Sp, for image feature extraction. Different from the conventional 2DPCA that is based on Frobenius-norm, 2DPCA-Sp learns an optimal projection matrix by maximizing the total scatter criterion based on Schatten p -norm in the low-dimensional feature space. Since p can take different values, 2DPCA-Sp is regarded as a general framework of 2DPCA. We also propose an iterative algorithm to solve the optimization problem of 2DPCA-Sp with 0 < p < 1 , which is simple, effective, and easy to implement. Experimental results on several popular image databases show that 2DPCA-Sp with 0 < p < 1 is robust to impact factors (e.g. illuminations, view directions, and expressions) of images. [ABSTRACT FROM AUTHOR]

A Schatten p -norm-based two-dimensional principal component analysis (2DPCA-SP) method was proposed for image feature extraction in our previous work. As an unsupervised method, 2DPCA-SP ignores the label information of training samples, which is essential to classification tasks. In this paper, we propose a novel Schatten p -norm-based two-dimensional discriminant analysis (2DDA-SP) method for image feature extraction, which learns an optimal projection matrix by maximizing the difference of Schatten p -norm-based between-class dispersion and Schatten p -norm-based within-class dispersion in low-dimensional feature space. By using both the Schatten p -norm metric and the label information of training samples, 2DDA-SP not only can efficiently extract discriminative features, but is also robust to outliers. We also propose an efficient iterative algorithm to solve the optimization problem of 2DDA-SP with 0 < p < 1 . Experimental results on several image databases show that 2DDA-SP with 0 < p < 1 is effective and robust for image feature extraction. [ABSTRACT FROM AUTHOR]