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

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. [ABSTRACT FROM AUTHOR]