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http://dx.doi.org/10.21289/KSIC.2020.23.3.515

Feature Extraction Using Convolutional Neural Networks for Random Translation  

Jin, Taeseok (Dept. of Mechatronics Ddongeo University)
Publication Information
Journal of the Korean Society of Industry Convergence / v.23, no.3, 2020 , pp. 515-521 More about this Journal
Abstract
Deep learning methods have been effectively used to provide great improvement in various research fields such as machine learning, image processing and computer vision. One of the most frequently used deep learning methods in image processing is the convolutional neural networks. Compared to the traditional artificial neural networks, convolutional neural networks do not use the predefined kernels, but instead they learn data specific kernels. This property makes them to be used as feature extractors as well. In this study, we compared the quality of CNN features for traditional texture feature extraction methods. Experimental results demonstrate the superiority of the CNN features. Additionally, the recognition process and result of a pioneering CNN on MNIST database are presented.
Keywords
Image recognition; CNN; Deep learning; MNIST. Image processing;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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