Feature selection based on discriminative power under uncertainty for computer vision applications

Abstract

Feature selection is a prolific research field, which has been widely studied in the last decades and has been successfully applied to numerous computer vision systems. It mainly aims to reduce the dimensionality and thus the system complexity. Features have not the same importance within the different classes. Some of them perform for class representation while others perform for class separation. In this paper, a new feature selection method based on discriminative power is proposed to select the relevant features under an uncertain framework, where the uncertainty is expressed through a possibility distribution. In an uncertain context, our method shows its ability to select features that can represent and discriminate between classes.