Unsupervised Machine Learning Via Transfer Learning and k-Means Clustering to Classify Materials Image Data
Unsupervised Machine Learning Via Transfer Learning and k-Means Clustering to Classify Materials Image Data
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DOI：
10.1007/s40192-021-00205-8
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发表时间：
2021-04-08
影响因子：
3.3
通讯作者：
Holm, Elizabeth
中科院分区：
文献类型：
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作者：
Cohn, Ryan;Holm, Elizabeth
无监督机器学习为从未标记数据集中提取知识和实现最大机器学习性能提供了重要机会。本文演示了如何构建，使用和评估一个高性能的无监督机器学习系统，用于对流行的微观结构数据集中的图像进行分类。东北大学钢铁表面缺陷数据库包括在热轧钢上观察到的六种不同缺陷的显微照片，其格式便于训练和评估图像分类模型。我们使用在自然图像的ImageNet数据集上预训练的VGG 16卷积神经网络来提取每个显微图的特征表示。在应用主成分分析从特征描述符中提取信号之后，我们使用k均值聚类来分类图像，而不需要标记的训练数据。该方法实现了99.4% +/- 0.16%的准确率，并且所得到的模型可以用于对新图像进行分类而无需重新训练。这种方法表明，与以前的研究相比，性能和实用性都有所改善。进行敏感性分析，以更好地了解每个步骤对分类性能的影响。这些结果为将无监督机器学习技术应用于材料科学中感兴趣的问题提供了见解。
Unsupervised machine learning offers significant opportunities for extracting knowledge from unlabeled datasets and for achieving maximum machine learning performance. This paper demonstrates how to construct, use, and evaluate a high-performance unsupervised machine learning system for classifying images in a popular microstructural dataset. The Northeastern University Steel Surface Defects Database includes micrographs of six different defects observed on hot-rolled steel in a format that is convenient for training and evaluating models for image classification. We use the VGG16 convolutional neural network pre-trained on the ImageNet dataset of natural images to extract feature representations for each micrograph. After applying principal component analysis to extract signal from the feature descriptors, we use k-means clustering to classify the images without needing labeled training data. The approach achieves 99.4% +/- 0.16% accuracy, and the resulting model can be used to classify new images without retraining. This approach demonstrates an improvement in both performance and utility compared to a previous study. A sensitivity analysis is conducted to better understand the influence of each step on the classification performance. The results provide insight toward applying unsupervised machine learning techniques to problems of interest in materials science.