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Characterization of Indian Visual Arts Architecture Ages and sub-ages using ML and Fuzzy-ML algorithms

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A Correction to this article was published on 11 October 2023

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Abstract

India has a huge treasure of Visual art Architecture. In recent years, more attention is done to the digitization of these Indian Visual Arts Architectures for promoting the travel and tourism of the country across the world. Digitization will create a huge repository of Visual Arts Architecture images. But there exists no methodology to explore images of these huge repositories. In this paper, two machine learning-based methods were proposed that utilize these huge, digitized repositories and try to characterize the age and sub-age of Indian architectures based on the historical ontology available in the historical books. The first method was based on classification and the second method was based on segmentation. Two different variants of convolution neural networks were combined with fuzzy logic for the proposed methods. To analyze the results of the proposed methods performance evaluation matrix classification accuracy was used. The classification accuracy of the second method was best compared to the first method.

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This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Madhav Institute of Technology & Science, Gola ka Mandir, Gwalior, 100190, Madhya Pradesh, India

    Amita Sharma & R. S. Jadon

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  1. Amita Sharma
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  2. R. S. Jadon
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These authors contributed equally to this work.

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Correspondence to Amita Sharma.

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R. S. Jadon contributed equally to this work.

The original online version of this article was revised: The first author’s surname "Sharma" was incorrectly spelled as "Shrarma" in the original publication of this article.

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Sharma, A., Jadon, R.S. Characterization of Indian Visual Arts Architecture Ages and sub-ages using ML and Fuzzy-ML algorithms. Multimed Tools Appl 82, 15493–15513 (2023). https://doi.org/10.1007/s11042-022-13926-5

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