Integrating Spatial and Discriminant Strength for Feature Selection and Linear Dimensionality Reduction

Abstract

Interest strength assignment to image points is important for selecting good features. Strength assignments using spatial information aim to detect interest points repeatable across different image/illumination transformations, and have been widely adopted in many interest point detectors. Recently, strength assignment schemes using discriminant information received attention, and studies showed the superiority of discriminant strength. In this paper, we introduce a strength assignment scheme integrating spatial and discriminant information, with the motivation that strong spatial information can be helpful in improving the robustness of the discriminant strength estimation, e.g., in undersampled training scenario. Our integrated strength uses a new discriminant strength assignment, so-called locality oriented Fisher criterion score. The integrated strength leads to new methods for feature selection and weighted linear dimensionality reduction. Experimental results in two case studies (embryo developmental stage classification and face recognition) show the favorable performance of the proposed methods.