Exploring Discriminative Features in Mueller Matrix Images for Electrospinning Classification

Abstract

Polarization images, which are captured in lights with different polarization angles, can extract more detail information about samples. Generally, there are two ways to make use of polarization images: direct processing of original polarization images and processing of Mueller matrix (MM) images. Since MM has clear physical meaning and each element in it represents a specific characteristic about samples, studying the relationship between the elements in MM and samples is a meaningful topic. In this paper, an importance sorting algorithm is proposed to explore discriminative elements in MM. Firstly, a linear weighted feature fusion method is proposed and three distances are defined to form the target function. Then, a convex quadratic programming model is built, with an algorithm to search the optimal solution. Finally, discriminative elements are choosed for classification according to the optimal weight vector. Experiments conducted on an electrospinning dataset show that the proposed method not only provides a consistent importance order of elements in MM, but also helps to find discriminative feature combinations for classification, which is useful for explaining of the polarization characteristics of samples. The source code is available at: https://github.com/madd2014/ImportanceSort.