Microstructure determines the most important factors that influence all aspects of the physical properties of the metal. Machine learning based systems allow us to look at the images to find the features of microstructure images which will be useful for classifying such images. These classification outcomes are the fundamental data for many material scientists. However, handcrafted feature vectors extracted by some means may involve a significant amount of irrelevant and redundant features, which may lead to misclassification of the microstructural images. In this paper, at first, a modified version of texture-based feature descriptor, Local Tetra Pattern (LTrP), which is named as Uniform variant of LTrP (ULTrP) is used to extract the features from the microstructural images. Then a feature selection algorithm based on Genetic Algorithm (GA), named as Diversification of Population (DP) in GA (DPGA), is proposed which is applied on ULTrP to remove the possible redundant features present therein. To assess fitness of the candidate solutions, instead of applying a learning algorithm, which is a common trend, the proposed DPGA uses an ensemble of three filter ranking methods. Impressive outcomes obtained by evaluating the proposed classification framework on a standard 7-class microstructural image dataset confirm its superiority over some state-of-the-art methods.

微观结构决定了影响金属物理性能各个方面的最重要因素。基于机器学习的系统使我们可以查看图像以找到微观结构图像的特征，这些特征将有助于对此类图像进行分类。这些分类结果是许多材料科学家的基础数据。但是，通过某些方式提取的手工特征向量可能涉及大量不相关和多余的特征，这可能导致微结构图像的错误分类。在本文中，首先，使用基于纹理的特征描述符的改进版本Local Tetra Pattern（LTrP）（称为LTrP的统一变体（ULTrP））从微结构图像中提取特征。然后基于遗传算法（GA）的特征选择算法，提议将其称为GA（DPGA）中的人口多样化（DP），将其应用于ULTrP，以消除其中存在的可能冗余特征。为了评估候选解决方案的适用性，建议的DPGA使用三种过滤器排序方法的集成，而不是应用一种普遍的趋势的学习算法。通过在标准的7类微结构图像数据集上评估建议的分类框架而获得的令人印象深刻的结果证实了其优于某些最新方法的优越性。