Abstract
Classifying architectural structures is an important and challenging task that requires expertise. Convolutional Neural Networks (CNN), which are a type of deep learning (DL) approach, have shown successful results in computer vision applications when combined with transfer learning. In this study, we utilized CNN based models to classify regional houses from Anatolia and Balkans based on their architectural styles with various pretrained models using transfer learning. We prepared a dataset using various sources and employed data augmentation and mixup techniques to solve the limited data availability problem for certain regional houses to improve the classification performance. Our study resulted in a classifier that successfully distinguishes 15 architectural classes from Anatolia and Balkans. We explain our predictions using grad-cam methodology.
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Acknowledgements
The research work presented in this workshop is a component of the research project with the identification number 2022IYTE-2-0029 and is aligned with the doctoral studies of Veli Mustafa Yönder.
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Yönder, V.M., İpek, E., Çetin, T., Çavka, H.B., Apaydın, M.S., Doğan, F. (2024). Classification of Turkish and Balkan House Architectures Using Transfer Learning and Deep Learning. In: Foresti, G.L., Fusiello, A., Hancock, E. (eds) Image Analysis and Processing - ICIAP 2023 Workshops. ICIAP 2023. Lecture Notes in Computer Science, vol 14366. Springer, Cham. https://doi.org/10.1007/978-3-031-51026-7_34
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