Indian classical dance action identification using adaptive graph matching from unconstrained videos

  • Abstract
  • Keywords
  • References
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  • Abstract

    Extracting and recognizing complex human movements from unconstraint online video sequence is a challenging task. In this work the problem becomes complicated by the use of unconstraint video sequences belonging to Indian classical dance forms. A new segmentation model is developed using discrete wavelet transform and local binary pattern features for segmentation. We also explore multiple feature fusion models with early fusion and late fusion techniques for improving the classification process. The extracted features were represented as a graph and a novel adaptive graph matching algorithm is proposed. We test the algorithms on online dance videos and on an Indian classical dance dataset prepared in our lab. The algorithms were tested for accuracy and correctness in identifying the dance postures.

  • Keywords

    Indian Classical Dance Identification, Adaptive Graph Matching, Feature Fusion, Histogram of Oriented Features (HOG), Discrete Wavelet Transform (DWT), Local Binary Patterns (LBP).

  • References

      [1] Poppe, Ronald. "A survey on vision-based human action recognition." Image and vision computing 28, no. 6 (2010): 976-990.

      [2] Chakravorty, Pallabi. "Hegemony, dance and nation: The construction of the classical dance in India." South Asia: Journal of South Asian Studies 21, no. 2 (1998): 107-120.

      [3] Rahmani, Hossein, Ajmal Mian, and Mubarak Shah. "Learning a deep model for human action recognition from novel viewpoints." IEEE Transactions on Pattern Analysis and Machine Intelligence (2017).

      [4] Dawn, Debapratim Das, and Soharab Hossain Shaikh. "A comprehensive survey of human action recognition with spatio-temporal interest point (STIP) detector." The Visual Computer 32, no. 3 (2016): 289-306.

      [5] Wang, Heng, and Cordelia Schmid. "Action recognition with improved trajectories." In Proceedings of the IEEE International Conference on Computer Vision, pp. 3551-3558. 2013.

      [6] Wang, Heng, Alexander Kläser, Cordelia Schmid, and Cheng-Lin Liu. "Action recognition by dense trajectories." In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pp. 3169-3176. IEEE, 2011.

      [7] Kishore, P. V. V., M. V. D. Prasad, D. Anil Kumar, and A. S. C. S. Sastry. "Optical Flow Hand Tracking and Active Contour Hand Shape Features for Continuous Sign Language Recognition with Artificial Neural Networks." In Advanced Computing (IACC), 2016 IEEE 6th International Conference on, pp. 346-351. IEEE, 2016.

      [8] Vatsyayan, Kapila. Indian classical dance. Ministry of Information and Broadcasting, Government of India, 1992.

      [9] Mohanty, Aparna, Pratik Vaishnavi, Prerana Jana, Anubhab Majumdar, Alfaz Ahmed, Trishita Goswami, and Rajiv R. Sahay. "Nrityabodha: Towards understanding Indian classical dance using a deep learning approach." Signal Processing: Image Communication 47 (2016): 529-548.

      [10] Yang, Yi, and Deva Ramanan. "Articulated pose estimation with flexible mixtures-of-parts." In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pp. 1385-1392. IEEE, 2011.

      [11] Wang, Fang, and Yi Li. "Beyond physical connections: Tree models in human pose estimation." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 596-603. 2013.

      [12] Samanta, Soumitra, Pulak Purkait, and Bhabatosh Chanda. "Indian classical dance classification by learning dance pose bases." In Applications of Computer Vision (WACV), 2012 IEEE Workshop on, pp. 265-270. IEEE, 2012.

      [13] K.V.V.Kumar, P.V.V.Kishore., “Indian Classical Dance Mudra Classification Using HOG Features and SVM Classifier” In Proceedings of International Conference on smart computing and information systems, Springer, India, 2017.

      [14] Fischler, Martin A., and Robert A. Elschlager. "The representation and matching of pictorial structures." IEEE Transactions on computers 100, no. 1 (1973): 67-92.

      [15] Kishore, P. V. V., Kumar, D. A., Sastry, A. S. C. S., & Kumar, E. K. (2018). Motionlets Matching with Adaptive Kernels for 3D Indian Sign Language Recognition. IEEE Sensors Journal, 1–1.

      [16] Isenor, D. K., and Safwat G. Zaky. "Fingerprint identification using graph matching." Pattern Recognition 19, no. 2 (1986): 113-122.

      [17] Sanfeliu, Alberto, and King-Sun Fu. "A distance measure between attributed relational graphs for pattern recognition." IEEE transactions on systems, man, and cybernetics 3 (1983): 353-362.

      [18] Bunke, Horst, and Kim Shearer. "A graph distance metric based on the maximal common subgraph." Pattern recognition letters 19, no. 3 (1998): 255-259.

      [19] Bougleux, Sébastien, Luc Brun, Vincenzo Carletti, Pasquale Foggia, Benoit Gaüzère, and Mario Vento. "Graph edit distance as a quadratic assignment problem." Pattern Recognition Letters 87 (2017): 38-46.

      [20] Jiang, Bo, Jin Tang, Xiaochun Cao, and Bin Luo. "Lagrangian relaxation graph matching." Pattern Recognition 61 (2017): 255-265.

      [21] Yang, Xu, Hong Qiao, and Zhi-Yong Liu. "Point correspondence by a new third order graph matching algorithm." Pattern Recognition 65 (2017): 108-118..

      [22] Ye, Dong, Yujun Yang, Bholanath Mandal, and Douglas J. Klein. "Graph invertibility and median eigenvalues." Linear Algebra and its Applications 513 (2017): 304-323.

      [23] Zheng, Qiang, Honglun Li, Baode Fan, Shuanhu Wu, Jindong Xu, and Zhulou Cao. "Modified localized multiplicative graph cuts based active contour model for object segmentation based on dynamic narrow band scheme." Biomedical Signal Processing and Control 33 (2017): 119-131.

      [24] Patel, Chirag I., Sanjay Garg, Tanish Zaveri, Asim Banerjee, and Ripal Patel. "Human action recognition using fusion of features for unconstrained video sequences." Computers & Electrical Engineering (2016).

      [25] Wang, Jin, Mary She, Saeid Nahavandi, and Abbas Kouzani. "A review of vision-based gait recognition methods for human identification." In Digital Image Computing: Techniques and Applications (DICTA), 2010 International Conference on, pp. 320-327. IEEE, 2010.

      [26] ping Tian, Dong. "A review on image feature extraction and representation techniques." International Journal of Multimedia and Ubiquitous Engineering 8, no. 4 (2013): 385-396.

      [27] Yang, Mingqiang, Kidiyo Kpalma, and Joseph Ronsin. "A survey of shape feature extraction techniques." (2008): 43-90.

      [28] Guo, Zhenhua, Lei Zhang, and David Zhang. "A completed modeling of local binary pattern operator for texture classification." IEEE Transactions on Image Processing 19, no. 6 (2010): 1657-1663.

      [29] Sinop, Ali Kemal, and Leo Grady. "A seeded image segmentation framework unifying graph cuts and random walker which yields a new algorithm." In Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on, pp. 1-8. IEEE, 2007.

      [30] Wang, Zeng-Fu, and Zhi-Gang Zheng. "A region based stereo matching algorithm using cooperative optimization." In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, pp. 1-8. IEEE, 2008.

      [31] Cheng, Shiyang, Ioannis Marras, Stefanos Zafeiriou, and Maja Pantic. "Statistical non-rigid ICP algorithm and its application to 3D face alignment." Image and Vision Computing 58 (2017): 3-12.

      [32] Zhou, Feng, and Fernando De la Torre. "Factorized graph matching." IEEE transactions on pattern analysis and machine intelligence 38, no. 9 (2016): 1774-1789.

      [33] Cawley, Gavin C., and Nicola LC Talbot. "Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers." Pattern Recognition 36, no. 11 (2003): 2585-2592.




Article ID: 10156
DOI: 10.14419/ijet.v7i1.1.10156

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