TY - JOUR
KW - Deep convolutional neural network (DCNN)
KW - Indian classical dance (ICD)
KW - Natya Shastra
KW - Residual network (ResNet50)
AU - Nikita Jain
AU - Vibhuti Bansal
AU - Deepali Virmani
AU - Vedika Gupta
AU - Lorenzo Salas-Morera
AU - Laura Garcia-Hernandez
AB - 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.
AN - WOS:000686182700001
BT - Applied Sciences (Switzerland)
DA - 2021/07//undefined
DB - Scopus
DO - 10.3390/app11146253
IS - 14
J2 - Appl. Sci.
LA - English
N2 - 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.
PY - 2021
SN - 20763417 (ISSN)
T2 - Applied Sciences (Switzerland)
TI - An Enhanced Deep Convolutional Neural Network for Classifying Indian Classical Dance Forms
UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110114045&doi=10.3390%2fapp11146253&partnerID=40&md5=23f40debdaf0de660fdbbc312b85b516
VL - 11
ER -