Comparative study of feature extraction algorithms for complex-valued gradient fields of digital images using linear dimensionality reduction methods
Egor Dmitriev, Vladislav Myasnikov · 2019
The paper presents an analysis of various approaches to constructing descriptions for gradient fields of digital images. The analyzed approaches are based on the well-known methods for reducing the data dimensionality, such as Principal (PCA) and Independent (ICA) Component Analysis, Linear Discriminant Analysis (LDA). We apply these methods not to original image, represented as a two-dimensional field of brightness (a halftone image), but to its secondary representation in the form of a two-dimensional gradient field, that is a complex-valued image. In this case, the approaches of using both the entire gradient field and only its phase part are analyzed. Also, the two independent ways of forming the original object final description are considered: using gradient field expansion coefficients in a derived basis and using the original author's method called model-oriented descriptors. The latter ones enable halving the number of real coefficients used in the original object description. The studies are conducted via solving face recognition problem. The effectiveness of the analyzed methods is demonstrated by applying them to images from Extended Yale Face Database B. The comparison is made using a nearest neighbor's classifier.