Deep Learning,Feature Learning,and Clustering Analysis for SEM Image Classification

作     者：Rossella Aversa Piero Coronica Cristiano De Nobili Stefano Cozzini Rossella Aversa;Piero Coronica;Cristiano De Nobili;Stefano Cozzini

作者机构：National Research Council-Istituto Officina dei Materiali(CNR-IOM)34136 TriesteItaly KIT-Karlsruhe Institute of TechnologyHermann-von-Helmholtz-Platz 176344 Eggenstein-LeopoldshafenGermany Research Software EngineeringUniversity of CambridgeCambridge CB30FAUK Freelance at *** Area Science ParkPadriciano 9934149 TriesteItaly 

出 版 物：《Data Intelligence》 (数据智能（英文）)

年 卷 期：2020年第2卷第4期

页      面：513-528页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 080401[工学-精密仪器及机械] 080203[工学-机械设计及理论] 0804[工学-仪器科学与技术] 0835[工学-软件工程] 0802[工学-机械工程] 0803[工学-光学工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work has been done within the NFFA-EUROPE project and has received funding from the European Union’s Horizon 2020 Research and Innovation Program under grant agreement No.654360 NFFA-EUROPE 

主　　题：Neural networks Feature learning Clustering analysis Scanning Electron Microscope(SEM) Image classification 

摘      要：In this paper,we report upon our recent work aimed at improving and adapting machine learning algorithms to automatically classify nanoscience images acquired by the Scanning Electron Microscope(SEM).This is done by coupling supervised and unsupervised learning *** first investigate supervised learning on a ten-category data set of images and compare the performance of the different models in terms of training ***,we reduce the dimensionality of the features through autoencoders to perform unsupervised learning on a subset of images in a selected range of scales(from 1μm to 2μm).Finally,we compare different clustering methods to uncover intrinsic structures in the images.