Comparative analysis of image classification algorithms based on traditional machine learning and deep learning
•Representative SVM and CNN algorithms in traditional machine learning and deep learning for research.•Under other conditions being the same, the data sets are different. The impact of the results varies.•This article compares and analyzes the accuracy and running time. Image classification is a hot...
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| Published in | Pattern recognition letters Vol. 141; pp. 61 - 67 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Amsterdam
Elsevier B.V
01.01.2021
Elsevier Science Ltd |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0167-8655 1872-7344 |
| DOI | 10.1016/j.patrec.2020.07.042 |
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| Abstract | •Representative SVM and CNN algorithms in traditional machine learning and deep learning for research.•Under other conditions being the same, the data sets are different. The impact of the results varies.•This article compares and analyzes the accuracy and running time.
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. |
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| AbstractList | •Representative SVM and CNN algorithms in traditional machine learning and deep learning for research.•Under other conditions being the same, the data sets are different. The impact of the results varies.•This article compares and analyzes the accuracy and running time.
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. 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. |
| Author | Fan, En Wang, Peng Wang, Pin |
| Author_xml | – sequence: 1 givenname: Pin surname: Wang fullname: Wang, Pin email: wangpin@vip.qq.com organization: School of Mechanical and Electrical Engineering, Shenzhen Polytechnic, Shenzhen 518055, Guangdong, China – sequence: 2 givenname: En surname: Fan fullname: Fan, En email: efan@szu.edu.cn, efan@szpt.edu.cn organization: Department of Computer Science and Engineering, Shaoxing University, Shaoxing 312000, Zhejiang, China – sequence: 3 givenname: Peng surname: Wang fullname: Wang, Peng email: sdhzdtwp@126.com organization: Garden Center, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, Guangdong,China |
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| Snippet | •Representative SVM and CNN algorithms in traditional machine learning and deep learning for research.•Under other conditions being the same, the data sets are... 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... |
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| SubjectTerms | Accuracy Algorithms Artificial neural networks Classification Comparative analysis Convolution Convolutional neural networks Datasets Deep learning Image classification Image processing Learning algorithms Machine learning Neural networks Support vector machines Traditional machine learning |
| Title | Comparative analysis of image classification algorithms based on traditional machine learning and deep learning |
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