Abstract
We propose two novel approaches to classify Indian monuments according to their distinct architectural styles. While the historical significance of most Indian monuments is well documented, the details of their architectural styles are not as well recorded. Different Indian architectural styles often show certain similar features which makes classification a difficult task. Previous work has focused on European architecture and standard datasets are available for the same, but no standard dataset exists for Indian architecture. Therefore, we have curated a dataset of Indian monuments. In this paper, we propose two approaches to classify monuments according to their styles: Radon Barcodes and Convolutional Neural Networks. The first approach is fast and consumes less memory, but the second approach gives an accuracy of 82%, which is better than the 76% accuracy of the first method.
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Sharma, S., Aggarwal, P., Bhattacharyya, A.N., Indu, S. (2018). Classification of Indian Monuments into Architectural Styles. In: Rameshan, R., Arora, C., Dutta Roy, S. (eds) Computer Vision, Pattern Recognition, Image Processing, and Graphics. NCVPRIPG 2017. Communications in Computer and Information Science, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-13-0020-2_47
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