An Enhanced Deep Convolutional Neural Network for Classifying Indian Classical Dance Forms
Source abstract
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 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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Human Pose and Action Recognition · Music and Audio Processing · Diversity and Impact of Dance
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi 110063, India
Department of Computer Science and Engineering, Bhagwan Parshuram Institute of Technology, New Delhi 110089, India
Area of Project Engineering, University of Córdoba, 14071 Córdoba, Spain
References (22)
Show source-provided references
1. Bharatanatyam and Mathematics: Teaching Geometry Through Dance10.5897/JFSA2015.0031 ↗
2. Rodriguez, G.E. Dare to Dance: Exploring Dance, Vulnerability, Anxiety and Communication. [Ph.D. Thesis, University of Texas].
3. Bisht, A., Bora, R., Saini, G., Shukla, P., and Raman, B. (2017, January 12–14). Indian Dance Form Recognition from Videos. Proceedings of the 2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), New Delhi, India.10.1109/SITIS.2017.30 ↗
4. Dong, Z., Shen, X., Li, H., and Tian, X. (2015). Photo Quality Assessment with DCNN that Understands Image Well. Proceedings of the International Conference on MultiMedia Modeling, Springer.10.1007/978-3-319-14442-9_57 ↗
5. Evgeniou, T., and Pontil, M. (2001). Support Vector Machines: Theory and Applications. Transactions on Petri Nets and Other Models of Concurrency XV, Springer.10.1007/3-540-44673-7_12 ↗
6. Kaushik, V., Mukherjee, P., and Lall, B. (2018, January 9–11). Nrityantar. Proceedings of the 11th Indian Conference on Computer Vision, Graphics and Image Processing, New Delhi, India.10.1145/3293353.3293419 ↗
7. Samanta, S., Purkait, P., and Chanda, B. (2012, January 9–11). Indian classical dance classification by learning dance pose bases. Proceedings of the 2012 IEEE Workshop on the Applications of Computer Vision (WACV), Breckenridge, CO, USA.10.1109/WACV.2012.6163050 ↗
8. Indian Classical Dance Classification with Adaboost Multiclass Classifier on Multifeature Fusion10.1155/2017/6204742 ↗
9. AdaBoost for Feature Selection, Classification and Its Relation with SVM, A Review10.1016/j.phpro.2012.03.160 ↗
10. Indian Classical Dance Mudra Classification Using HOG Features and SVM Classifier10.11591/ijece.v7i5.pp2537-2546 ↗
11. Evaluation of transfer learning of pre-trained CNNs applied to breast cancer detection on infrared images10.1364/AO.386037 ↗
12. Brox, T., and Malik, J. (2010, January 3). Object Segmentation by Long Term Analysis of Point Trajectories. Proceedings of the Transactions on Petri Nets and Other Models of Concurrency XV, Cachan, France.
13. Dimitriou, N., and Delopoulos, A. (October, January 30). Improved motion segmentation using locally sampled subspaces. Proceedings of the 2012 19th IEEE International Conference on Image Processing, Orlando, FL, USA.10.1109/ICIP.2012.6466857 ↗
14. Ochs, P., and Brox, T. (2011, January 6–13). Object segmentation in video: A hierarchical variational approach for turning point trajectories into dense regions. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain.10.1109/ICCV.2011.6126418 ↗
15. Motion-based segmentation of objects using overlapping temporal windows10.1016/j.imavis.2013.06.005 ↗
16. Jain, N., Gupta, V., Shubham, S., Madan, A., Chaudhary, A., and Santosh, K.C. (2021). Understanding cartoon emotion using integrated deep neural network on large dataset. Neural Comput. Appl., 1–21.10.1007/s00521-021-06003-9 ↗
17. Dewan, S., Shubham, A., and Navjyoti, S. (2017, January 13–15). A deep learning pipeline for Indian dance style classification. Tenth International Conference on Machine Vision (ICMV 2017), Vienna, Austria.10.1117/12.2309445 ↗
18. Indian Classical Dance Action Identification and Classification with Convolutional Neural Networks10.1155/2018/5141402 ↗
19. A survey of thresholding techniques10.1016/0734-189X(88)90022-9 ↗
20. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
21. Cell image analysis for malaria detection using deep convolutional network
22. Emotion recognition of audio/speech data using deep learning approaches