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논문 기본 정보

자료유형
학술저널
저자정보
Van Hiep Phung (Hanbat National University) Eun Joo Rhee (Hanbat National University)
저널정보
한국정보통신학회JICCE Journal of information and communication convergence engineering Journal of information and communication convergence engineering Vol.16 No.3
발행연도
2018.9
수록면
173 - 178 (6page)

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초록· 키워드

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Accurate classification of cloud images is a challenging task. Almost all the existing methods rely on hand-crafted feature extraction. Their limitation is low discriminative power. In the recent years, deep learning with convolution neural networks (CNNs), which can auto extract features, has achieved promising results in many computer vision and image understanding fields. However, deep learning approaches usually need large datasets. This paper proposes a deep learning approach for classification of cloud image patches on small datasets. First, we design a suitable deep learning model for small datasets using a CNN, and then we apply data augmentation and dropout regularization techniques to increase the generalization of the model. The experiments for the proposed approach were performed on SWIMCAT small dataset with k-fold cross-validation. The experimental results demonstrated perfect classification accuracy for most classes on every fold, and confirmed both the high accuracy and the robustness of the proposed model.

목차

Abstract
I. INTRODUCTION
II. SWIMCAT DATASET
III. METHODS
IV. EXPERIMENT
V. CONCLUSION
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