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Chao Yin

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Proceedings Article | 10 February 2023 Paper

Architectural style classification of the Chinese traditional settlements using deep learning

Qing Han, Chao Yin

Proceedings Volume 12552, 125522X (2023) https://doi.org/10.1117/12.2667749

KEYWORDS: Deep learning, Performance modeling, Data modeling, Education and training, Overfitting, Image classification, Machine learning, Matrices, Convolutional neural networks, Visualization

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This paper investigates state-of-the-art deep learning techniques to achieve automatic architectural style classification of the Chinese traditional settlements. First, a new annotated dataset is built with six typical Chinese architectural styles, consisting of over 1000 web-crawled images and an original image collection of Chinese traditional settlements. Second, a state-of-the-art convolutional network named DenseNet is benchmarked on the new dataset to learn the effectiveness of the deep learning networks. Yet, the DenseNet network suffered server overfitting on the small-sized new dataset. Third, to overcome the common overfitting problem, a new deep learning framework named DenseNet-TL-Aug is developed by leveraging transfer learning (TL) and data augmentation (DA) techniques, e.g., AutoAugment. The experimental results demonstrate that the new developed framework achieves much better classification performance in classifying the Chinese traditional style images than the original DenseNet, significantly mitigating the overfitting problem. This study will contribute to automated landscape gene recognition as well as the design and development of traditional tourism.

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