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Keywords = Natya Shastra

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14 pages, 19881 KiB  
Article
An Enhanced Deep Convolutional Neural Network for Classifying Indian Classical Dance Forms
by Nikita Jain, Vibhuti Bansal, Deepali Virmani, Vedika Gupta, Lorenzo Salas-Morera and Laura Garcia-Hernandez
Appl. Sci. 2021, 11(14), 6253; https://doi.org/10.3390/app11146253 - 6 Jul 2021
Cited by 38 | Viewed by 5829
Abstract
Indian classical dance (ICD) classification is an interesting subject because of its complex body posture. It provides a stage to experiment with various computer vision and deep learning concepts. With a change in learning styles, automated teaching solutions have become inevitable in every [...] Read more.
Indian classical dance (ICD) classification is an interesting subject because of its complex body posture. It provides a stage to experiment with various computer vision and deep learning concepts. With a change in learning styles, automated teaching solutions have become inevitable in every field, from traditional to online platforms. Additionally, ICD forms an essential part of a rich cultural and intangible heritage, which at all costs must be modernized and preserved. In this paper, we have attempted an exhaustive classification of dance forms into eight categories. For classification, we have proposed a deep convolutional neural network (DCNN) model using ResNet50, which outperforms various state-of-the-art approaches. Additionally, to our surprise, the proposed model also surpassed a few recently published works in terms of performance evaluation. The input to the proposed network is initially pre-processed using image thresholding and sampling. Next, a truncated DCNN based on ResNet50 is applied to the pre-processed samples. The proposed model gives an accuracy score of 0.911. Full article
(This article belongs to the Special Issue Human-Computer Interaction for Industrial Applications)
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