CNN study of convolutional neural networks in classification and feature extraction applications

Yufeng Zheng; Hongyu Wang; Yingguang Hao

doi:10.1117/12.2560372

21 April 2020 CNN study of convolutional neural networks in classification and feature extraction applications

Yufeng Zheng, Hongyu Wang, Yingguang Hao

Proceedings Volume 11395, Big Data II: Learning, Analytics, and Applications; 113950K (2020) https://doi.org/10.1117/12.2560372
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Convolutional neural networks (CNNs) become very useful tools in classification and feature extraction applications. In this research, we present a comparable study of several commonly-used CNNs in terms of performance. Most recently developed CNNs are selected in our study, which include NASNet-Large, Inception-Resnet-v2, DenseNet201, NASNet- Mobile, MovileNet-v2 as well as well-known ResNet50 and VGG19 for comparisons. In our classification experiments there are eight different geometrical shapes, each of which includes 486 to 620 computer-generated images. Two basic shapes, triangle and square, vary with solid or hollow shapes, and then overlapping with or without three-disk distractors. CNNs training and testing both can use the shape images as the experiments conducted on the ImageNet. On the other hand, we can use the pretrained CNNs on ImageNet to extract features, then train a multiclass support vector machine (SVM) to do classification. Training images may include four shapes or two categories (solid or hollow), while testing images are four shapes or two categories with distractors. The performance of CNNs includes classification accuracies and time costs in training and testing. The experimental results will provide guidance in selecting CNN models.

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Citation Download Citation

Yufeng Zheng, Hongyu Wang, and Yingguang Hao "CNN study of convolutional neural networks in classification and feature extraction applications", Proc. SPIE 11395, Big Data II: Learning, Analytics, and Applications, 113950K (21 April 2020); https://doi.org/10.1117/12.2560372

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