Feature Selection and Dimensionality Reduction for Efficient BharataNatyam Dance Classification Sangeeta Jadhav, Manish Joshi & Jyoti Pawar S. S. Dempo College; Goa, North Maharashtra University; Jalgaon, Goa University; Goa.
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Abstract Choreography is an intuitive domain. Various automotive techniques have been used for Western dance notations, its animation as well as for choreographic skills. Indian Classical Dance, especially BharataNatyam (BN) has been a subject of fascination both in the east as well as the west. We have attempted to aid the choreographer for this creative domain of choreography for pure dance movements in BN called Nritta. The results of our ArtToSMart (System Modelled art) system have been very promising. We have successfully designed a unique fitness function for the Genetic Algorithm to generate new poses for a given beat. The validation of these results, through dance experts from various states of India, has given us a positive direction. We have validated these results through the Mean Opinion Score method. We have tried to find the best features from the thirty attribute vector for dimensionality reduction which will further help us in generating the reducts and decision rules. Since the enumerated dance poses by the system are too many and also the evaluation of our results by dance experts have been very subjective, we have used Rough Set tools to automatically classify the dance poses. The results are promising and have about 72.7 % accuracy. We have experimented further with the tool to train the system for getting better accuracy for classification. Thus a comparative study has been done with two different tools namely WEKA 3.6.11 and RSES 2.2.2.
Introduction to Choreographic automation
Tools Used • Rough Set Exploration System 2.2.2 • Waikato Environment for KnowledgeAnalysis 3.6.11
RSES AND WEKA Experimental Results • Using RSES, with cross-validation method, the system has generated 72.7 % accuracy for the trained data. • Using WEKA , J48 Algorithm, 66.96 % accuracy • Choosing only the 8 attributes given as reducts from the RSES tool • depending on various coverage like 11,9,3,2 and 1 respectively for all the classes • the results showed 70.98 % accuracy.
• Modeling of the human body [5], [4] • Single Beat Choreography through Genetic Algorithm [2] • Stick Figure Generation [1] • Dance Experts evaluation of the results • Mean Opinion Score method used • Multi Beat Choreography [3]
Figure 3: WEKA accuracy results with 8 attributes
Conclusions and Forthcoming Research • The reducts obtained from RSES were used for the purpose of comparison with another tool WEKA and we got promising results here too. Figure 1: Art to SMart Multibeat Choreography User Interface
• The system can be trained further to get more accuracy and later be tested with the enumerated data. • Experts from the domain can validate the results and hence we can get a robust choreographic tool for BN.
Main Objectives 1. Automatic Classification of BN dance poses 2. Reducing Complexity of the Classifier model 3. Feature Selection (using Reducts) 4. Determine the effect of dimensionality reduction
References [1] Sangeeta Jadhav, Anwaya Aras, Manish Joshi, and Jyoti Pawar. An automated stick figure generation for bharatanatyam dance visualization. In Proceedings of the 2014 International Conference on Interdisciplinary Advances in Applied Computing, ICONIAAC ’14, pages 12:1–12:8, NY, USA, 2014. ACM. [2] Sangeeta Jadhav, Manish Joshi, and Jyoti Pawar. Art to SMart: an evolutionary computational model for BharataNatyam choreography. In Proceedings of the 12th Conference on Hybrid Intelligent Systems (HIS), pages 384–389. IEEE Xplore, December 2012. [3] Sangeeta Jadhav, Manish Joshi, and Jyoti Pawar. Art to smart: Automation for bharatanatyam choreography. In 19th International Conference on Management of Data, COMAD 2013, Ahmedabad, India, December 19-21, 2013, pages 131–134, 2013. [4] Sangeeta Jadhav and M. Sasikumar. A computational model for BharataNatyam choreography. In (IJCSIS) International Journal of Computer Science and Information Security, volume Vol. 8, No. 7, page 231233, October 2010.
Figure 2: Dance Pose
Complete Dance Vector for Fig. 2 [1,1,1,4,0,0,0,-1,0,0,1,3,0,1,3,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0] Head(mudra,orientn), RightHand(mudra,x,y,z,elbow,wrist,palm,shoulder), LeftHand(mudra,x,y,z,elbow,wrist,palm,shoulder), Waist(twist,bend), RightLeg(ankle,knee,x,y,z), LeftLeg(ankle,knee,x,y,z)
[5] Sangeeta Jadhav, Manish Joshi, and Jyoti Pawar. Modelling BharataNatyam dance steps: Art to smart. In Proceedings of the CUBE International IT conference & Exhibition Proceedings, PUNE, September 2012. ACM DL.
Acknowledgements This work was supported by UGC under Major Research Project No 39-901/2010(SR) (February 2011 to February 2014).
