Indian Classical Dance Mudra Classification Using HOG Features and SVM Classifier

@article{Kumar2017IndianCD,
  title={Indian Classical Dance Mudra Classification Using HOG Features and SVM Classifier},
  author={K. V. V. Kumar and Polurie Venkata Vijay Kishore},
  journal={International Journal of Electrical and Computer Engineering},
  year={2017},
  volume={7},
  pages={2537-2546}
}
  • K. V. V. Kumar, P. Kishore
  • Published 1 October 2017
  • Computer Science
  • International Journal of Electrical and Computer Engineering
Digital understanding of Indian classical dance is least studied work, though it has been a part of Indian Culture from around 200BC. This work explores the possibilities of recognizing classical dance mudras in various dance forms in India. The images of hand mudras of various classical dances are collected form the internet and a database is created for this job.  Histogram of oriented (HOG) features of hand mudras input the classifier. Support vector machine (SVM) classifies the HOG features… 
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29 Citations
Highly Influential Citations
3
Background Citations
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Methods Citations
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Results Citations
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29 Citations

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A new segmentation model is developed using discrete wavelet transform and local binary pattern (LBP) features for segmentation and multiple feature fusion models with early fusion during segmentation stage and late fusion after segmentation for improving the classification process.
A Comparative Study of Certain Classifiers for Bharatanatyam Mudra Images' Classification using Hu-Moments
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A 3-stage methodology for the classification of Bharatanatyam mudras is presented and rule-based classifiers, artificial neural networks, and k-nearest neighbor classifiers are used for the classified of unknown mudras.
Indian Classical Dance Action Identification and Classification with Convolutional Neural Networks
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This paper proposes the classification of Indian classical dance actions using a powerful artificial intelligence tool: convolutional neural networks (CNN), and achieves a 93.33% recognition rate compared to other classifiers reported on the same dataset.
Stratification of Indian Dance Forms Through Audio Signal
TLDR
Support vector machine or SVM, neural network or NN and Naive Bayes-based classifier are employed for classification of Indian dance forms, and classification accuracy obtained here is compared with classification accuracy of other preceding works done to echo the effectiveness of the projected set of features.
A Comparative Study of Suitability of Certain Features in Classification of Bharatanatyam Mudra Images Using Artificial Neural Network
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A 3-stage methodology for classification of single hand mudra images using convolutional neural network is presented and finds application in e-learning of ‘Bharatanatyam’ dance in particular and dances in general and automation of commentary during concerts.
Indian classical dance action identification using adaptive graph matching from unconstrained videos
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A new segmentation model is developed using discrete wavelet transform and local binary pattern features for segmentation and multiple feature fusion models with early fusion and late fusion techniques are explored for improving the classification process.
On the Classification of Kathakali Hand Gestures Using Support Vector Machines and Convolutional Neural Networks
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This paper generated a dataset for Kathakali hand gestures, proposed a Support Vector Machine (SVM) model and Convolutional Neural Network (CNN) model which classify the images into 24 different classes, and compared the performance of machine learning algorithms and deep learning algorithms.
An Enhanced Deep Convolutional Neural Network for Classifying Indian Classical Dance Forms
TLDR
An exhaustive classification of dance forms into eight categories is attempted using a deep convolutional neural network model using ResNet50, which outperforms various state-of-the-art approaches and surpassed a few recently published works in terms of performance evaluation.
Buddhist Hasta Mudra Recognition Using Morphological Features
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A framework that recognizes the Buddhist hand gesture or Hasta Mudra is provided and the experimental results show that utilising geometric features and using k-Nearest Neighbor (kNN) as a classifier, an approximately 70% recognition rate is achieved.
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