首页    期刊浏览 2024年12月12日 星期四
登录注册

文章基本信息

  • 标题:Indian Classical Dance Mudra Classification Using HOG Features and SVM Classifier
  • 本地全文:下载
  • 作者:K.V.V. Kumar ; P.V.V. Kishore
  • 期刊名称:International Journal of Electrical and Computer Engineering
  • 电子版ISSN:2088-8708
  • 出版年度:2017
  • 卷号:7
  • 期号:5
  • 页码:2537-2546
  • DOI:10.11591/ijece.v7i5.pp2537-2546
  • 语种:English
  • 出版社:Institute of Advanced Engineering and Science (IAES)
  • 摘要: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 into mudras as text messages. The mudra recognition frequency (MRF) is calculated for each mudra using graphical user interface (GUI) developed from the model. Popular feature vectors such as SIFT, SURF, LBP and HAAR are tested against HOG for precision and swiftness. This work helps new learners and dance enthusiastic people to learn and understand dance forms and related information on their mobile devices.
  • 其他摘要: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 into mudras as text messages. The mudra recognition frequency (MRF) is calculated for each mudra using graphical user interface (GUI) developed from the model. Popular feature vectors such as SIFT, SURF, LBP and HAAR are tested against HOG for precision and swiftness. This work helps new learners and dance enthusiastic people to learn and understand dance forms and related information on their mobile devices.
  • 关键词:Indian classical dance mudras;HOG Features;Mudra Recognition Frequency;Scale invariant features;SVM classifier
国家哲学社会科学文献中心版权所有