Materials with similar microstructural features exhibit similar properties, a fact which often provides useful insights for a detailed understanding of the materials. An analysis of material similarity in terms of microstructural images is proposed for predicting some properties of interest. This similarity analysis is inspired by the application of medical image retrieval to guide diagnostic decisions. Some relevant analyzing techniques including machine‐learning algorithms of zero‐normalized cross‐correlation, mutual information, maximum likelihood estimation, principal component analysis, and self‐organizing map are applied in this work. These techniques are systematically employed to identify the variances of the query images based on the metrics of image natural properties (such as brightness which is measured in pixel) or metallurgical features contained in the microstructural images. It is shown that the employed methods exhibit consistent similarity evaluation results. The proposed similarity analysis of microstructural images is expected to provide a new avenue for understanding the materials paradigm.

中文翻译：

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基于机器学习的图像相似性分析，用于材料表征

具有相似的微观结构特征的材料表现出相似的特性，这一事实通常可以为深入了解材料提供有用的见解。提出了一种根据微观结构图像对材料相似性进行分析的方法，以预测某些感兴趣的特性。这种相似性分析是受医学图像检索在指导诊断决策中的应用启发的。这项工作采用了一些相关的分析技术，包括零归一化互相关的机器学习算法，互信息，最大似然估计，主成分分析和自组织映射。这些技术被系统地用于基于图像自然属性（例如，以像素为单位测量的亮度）或包含在微结构图像中的冶金特征的度量来识别查询图像的方差。结果表明，所采用的方法具有相似的评价结果​​。拟议的微观结构图像相似性分析有望为理解材料范例提供一条新途径。