Skip to main content
SpringerLink
Log in

A Comparative Study of Suitability of Certain Features in Classification of Bharatanatyam Mudra Images Using Artificial Neural Network

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Bharatanatyam is an Indian classical dance, which is composed of various body postures and hand gestures. This ancient art of dance has to be studied under the supervision of experts but at present there is dearth of Bharatanatyam dance experts. This has led to take leverage of technology to make this dance self pursuable. Thus, it is the motivation for automation of identification of mudras through image processing. This paper presents a 3-stage methodology for classification of single hand mudra images. The first stage involves acquisition and preprocessing of images of mudras to obtain contours of mudras using canny edge detector. In the second stage, the features, namely, Hu-moments, eigenvalues and intersections are extracted. In the third stage artificial neural network is used for classification of mudras. The comparative study of classification accuracies of using different features is provided at the end. To corroborate the obtained classification accuracies, a deep learning approach, namely, convolutional neural network is adopted. The work finds application in e-learning of ‘Bharatanatyam’ dance in particular and dances in general and automation of commentary during concerts.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22

Similar content being viewed by others

References

  1. Anami BS, Bhandage VA (2018) A vertical-horizontal-intersections feature based method for identification of bharatanatyam double hand mudra images. Multimed Tools Appl. https://doi.org/10.1007/s11042-018-6223-y

    Google Scholar 

  2. Liu W, Ma X, Zhou Y, Tao D, Cheng J (2018) p-Laplacian regularization for scene recognition. IEEE Trans Cybern 99:1–14. https://doi.org/10.1109/TCYB.2018.2833843

    Google Scholar 

  3. Yu J, Yang X, Gao F, Tao D (2017) Deep multimodal distance metric learning using click constraints for image ranking. IEEE Trans Cybern 47(12):4014–4024. https://doi.org/10.1109/TCYB.2016.2591583

    Article  Google Scholar 

  4. Kumar KVV, Kishore PVV (2017) Indian classical dance mudra classification using HOG features and SVM classifier. Int J Electr Comput Eng (IJECE) 7(5):2537–2546. https://doi.org/10.11591/ijece.v7i1

    Article  Google Scholar 

  5. Solís F, Martínez D, Espinoza O (2016) Automatic Mexican sign language recognition using normalized moments and artificial neural networks. Engineering 8:733–740. https://doi.org/10.4236/eng.2016.810066

    Article  Google Scholar 

  6. Zadghorban M, Nahvi M (eds) (2016) An algorithm on sign words extraction and recognition of continuous Persian sign language based on motion and shape features of hands. In: Pattern analysis and applications. Springer, Berlin, pp 1–13

  7. Wang M, Chen W-Y, Li XD (2016) Hand gesture recognition using valley circle feature and Hu’s moments technique for robot movement control. Measurement 94:734–744

    Article  Google Scholar 

  8. Pradhan A, Kumar S, Dhakal D, Pradhan B (2016) Implementation of PCA for recognition of hand gesture representing alphabets. Int J Adv Res Comput Sci Softw Eng 6(3):263–268

    Google Scholar 

  9. Hong C, Yu J, Wan J, Tao D, Wang M (2015) Multimodal deep autoencoder for human pose recovery. IEEE Trans Image Process 24(12):5659–5670. https://doi.org/10.1109/TIP.2015.2487860

    Article  MathSciNet  MATH  Google Scholar 

  10. Fagiani M, Principi E, Squartini S, Piazza F (2015) Signer independent isolated Italian sign recognition based on hidden Markov models. Pattern Anal Appl 18(2):385–402

    Article  MathSciNet  Google Scholar 

  11. Fernando M, Wijayanayake J (2015) Novel approach to use HU moments with image processing techniques for real time sign language communication. Int J Image Process (IJIP) 9(6):335

    Google Scholar 

  12. Nguyen T-N, Huynh H-H (2015) Static hand gesture recognition using principal component analysis combined with artificial neural network. J Autom Control Eng 3(1):40–45

    Article  Google Scholar 

  13. Dixit K, Jalal AS (2013) Automatic Indian sign language recognition system. In: 2013 IEEE 3rd international advance computing conference (IACC). IEEE, pp 883–887

  14. Premaratne P, Yang S, Zou Z, Vial P (2013) Australian sign language recognition using moment invariants. In: International conference on intelligent computing, LNAI 7996. Springer, Berlin, pp 509–514

  15. Sriparna S, Ghosh L, Konar A, Janarthanan R (2013) Fuzzy L membership function based hand gesture recognition for Bharatanatyam dance. In: 2013 5th international conference on computational intelligence and communication networks (CICN). IEEE, pp 331–335

  16. Adithya V, Vinod PR, Usha G (2013) Artificial neural network based method for Indian sign language recognition. In: Proceedings of 2013 IEEE conference on information and communication technologies (ICT 2013) Jeju Island, pp 1080–1085, April 2013

  17. Singha J, Das K (2013) Indian sign language recognition using eigen value weighted euclidean distance based classification technique. Int J Adv Comput Sci Appl (IJACSA) 4(2):188–195

    Google Scholar 

  18. Liu Y, Yin Y, Zhang S (2012) Hand gesture recognition based on HU moments in interaction of virtual reality. In: 4th international conference on intelligent human-machine systems and cybernetics (IHMSC), vol 1. IEEE, pp 145–148

  19. Otiniano-Rodríguez KC, Cámara-Chávez G, Menotti D (2012) Hu and Zernike moments for sign language recognition. In: Proceedings of the international conference on image processing, computer vision, and pattern recognition (IPCV), p 1

  20. Hariharan D, Acharya T, Mitra S (2011) Recognizing hand gestures of a dancer. In: Kuznetsov SO, Mandal DP, Kundu MK, Pal SK (eds) Pattern recognition and machine intelligence. PReMI 2011. Lecture notes in computer science, vol 6744. Springer, Berlin

  21. Zaki MM, Shaheen SI (2011) Sign language recognition using a combination of new vision based features. Pattern Recognit Lett 32(4):572–577. https://doi.org/10.1016/j.patrec.2010.11.013

    Article  Google Scholar 

  22. Sharma P, Aneja A, Kumar A, Kumar S (2011) Face recognition using neural network and eigen values with distinct block processing. Int J Sci Eng Res 2(5). ISSN 2229-5518

  23. Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. In: Proceedings of the IEEE, pp 2278–2324

  24. Hu M-K (1962) Visual pattern recognition by moment invariants. IEEE Trans Inf Theory 8:179–187

    MATH  Google Scholar 

Download references

Acknowledgements

The authors wish to acknowledge Bharatanatyam dance teachers, namely, Mr. Gajanan V. and Ms. Mala T., Kala Kutir, Gadag, Karntaka, India and Mrs. Nandini S. Diwan, D/O. Pandit Badrinath Kulkarni, Prakash Nritya kala Mandir, Kolhapur,Maharstra, India for allowing us to capture images. Consent is also taken from the artist involved in the images.

Author information

Authors and Affiliations

  1. K.L.E. Institute of Technology, Hubballi, Karnataka, India

    Basavaraj S. Anami

  2. Tontadarya College of Engineering, Gadag, Karnataka, India

    Venkatesh A. Bhandage

Authors
  1. Basavaraj S. Anami
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Venkatesh A. Bhandage
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Venkatesh A. Bhandage.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Anami, B.S., Bhandage, V.A. A Comparative Study of Suitability of Certain Features in Classification of Bharatanatyam Mudra Images Using Artificial Neural Network. Neural Process Lett 50, 741–769 (2019). https://doi.org/10.1007/s11063-018-9921-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-018-9921-6

Keywords

Search

Navigation