文献类型：期刊文献

英文题名：Comparative analysis of image classification algorithms based on traditional machine learning and deep learning

作者：Wang, Pin[1];Fan, En[2];Wang, Peng[3]

机构：[1]Shenzhen Polytech, Sch Mech & Elect Engn, Shenzhen 518055, Guangdong, Peoples R China;[2]Shaoxing Univ, Dept Comp Sci & Engn, Shaoxing 312000, Zhejiang, Peoples R China;[3]Chinese Acad Sci, Garden Ctr, South China Bot Garden, Guangzhou 510650, Guangdong, Peoples R China

年份：2021

卷号：141

起止页码：61

外文期刊名：PATTERN RECOGNITION LETTERS

收录：SCI-EXPANDED(收录号：WOS:000606751200009)、、EI(收录号：20205209695262)、Scopus(收录号：2-s2.0-85098123393)、WOS

语种：英文

外文关键词：Traditional machine learning; Deep learning; Support vector machines; Convolutional neural networks

外文摘要：Image classification is a hot research topic in today's society and an important direction in the field of image processing research. SVM is a very powerful classification model in machine learning. CNN is a type of feedforward neural network that includes convolution calculation and has a deep structure. It is one of the representative algorithms of deep learning. Taking SVM and CNN as examples, this paper compares and analyzes the traditional machine learning and deep learning image classification algorithms. This study found that when using a large sample mnist dataset, the accuracy of SVM is 0.88 and the accuracy of CNN is 0.98; when using a small sample COREL1000 dataset, the accuracy of SVM is 0.86 and the accuracy of CNN is 0.83. The experimental results in this paper show that traditional machine learning has a better solution effect on small sample data sets, and deep learning framework has higher recognition accuracy on large sample data sets. (C) 2020 Published by Elsevier B.V.