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Article

An Enhanced Deep Convolutional Neural Network for Classifying Indian Classical Dance Forms

1
Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi 110063, India
2
Department of Computer Science and Engineering, Bhagwan Parshuram Institute of Technology, New Delhi 110089, India
3
Area of Project Engineering, University of Córdoba, 14071 Córdoba, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Christian W. Dawson
Appl. Sci. 2021, 11(14), 6253; https://doi.org/10.3390/app11146253
Received: 17 May 2021 / Revised: 10 June 2021 / Accepted: 21 June 2021 / Published: 6 July 2021
(This article belongs to the Special Issue Human-Computer Interaction for Industrial Applications)
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. View Full-Text
Keywords: deep convolutional neural network (DCNN); Indian classical dance (ICD); residual network (ResNet50); Natya Shastra deep convolutional neural network (DCNN); Indian classical dance (ICD); residual network (ResNet50); Natya Shastra
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MDPI and ACS Style

Jain, N.; Bansal, V.; Virmani, D.; Gupta, V.; Salas-Morera, L.; Garcia-Hernandez, L. An Enhanced Deep Convolutional Neural Network for Classifying Indian Classical Dance Forms. Appl. Sci. 2021, 11, 6253. https://doi.org/10.3390/app11146253

AMA Style

Jain N, Bansal V, Virmani D, Gupta V, Salas-Morera L, Garcia-Hernandez L. An Enhanced Deep Convolutional Neural Network for Classifying Indian Classical Dance Forms. Applied Sciences. 2021; 11(14):6253. https://doi.org/10.3390/app11146253

Chicago/Turabian Style

Jain, Nikita, Vibhuti Bansal, Deepali Virmani, Vedika Gupta, Lorenzo Salas-Morera, and Laura Garcia-Hernandez. 2021. "An Enhanced Deep Convolutional Neural Network for Classifying Indian Classical Dance Forms" Applied Sciences 11, no. 14: 6253. https://doi.org/10.3390/app11146253

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