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Indian Classical Dance Mudra Classification Using HOG Features and SVM Classifier
Ist Teil von
International journal of electrical and computer engineering (Malacca, Malacca), 2017-10, Vol.7 (5), p.2537
Ort / Verlag
Yogyakarta: IAES Institute of Advanced Engineering and Science
Erscheinungsjahr
2017
Link zum Volltext
Quelle
EZB Electronic Journals Library
Beschreibungen/Notizen
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.