Abstract
Culture and arts are integral parts of human society and evolution. We find ancient cave paintings which prove even our early ancestors were thinking similar to modern human. Any civilized society creates arts and monuments to reflect their beliefs and ideas. Today’s vibrant culture is tomorrow’s archaeology due to the natural or human-made destruction of the sites. The human race must preserve the current and the prior arts and artifacts from such damage and illegal means of trade. To do so, we need a considerable number of skilled human resources as well as person-hours. Photography plays a vital role in monitoring and cataloging such arts and sculptures in real-time. However, it requires specialized human interventions, which are not always possible. The alternative is an automated deep learning-based image classification technique to discriminate different arts and sculptures more efficiently. Real-time classification and recognition can be used to identify and preserve arts and sculptures in a more efficient way. Here, we have used different deep learning-based transfer learning models to classify the Indic, Egyptian, and Italian sculpture images. We have examined our techniques on popular Kaggle Arts-Image dataset to distinguish five types of arts: drawings, engraving, iconography, painting, and sculpture. Our trained models achieve 98.15% and 97.36% accuracies for the aforementioned sculptures and Kaggle datasets, respectively.
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Chatterjee, R., Chatterjee, A., Halder, R. (2021). Impact of Deep Learning on Arts and Archaeology: An Image Classification Point of View. In: Prateek, M., Singh, T.P., Choudhury, T., Pandey, H.M., Gia Nhu, N. (eds) Proceedings of International Conference on Machine Intelligence and Data Science Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-4087-9_65
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DOI: https://doi.org/10.1007/978-981-33-4087-9_65
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