Classification of Indian Dance Forms using Pre-Trained Model-VGG

被引：0

作者：

Biswas, Snigdha ^{[1
]}

Ghildiyal, Anirudh ^{[1
]}

Sharma, Sachin ^{[1
]}

机构：

[1] Graph Era Deemed Univ, Dept Comp Sci & Engn, Dehra Dun, Uttarakhand, India

来源：

2021 SIXTH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, SIGNAL PROCESSING AND NETWORKING (WISPNET) | 2021年

关键词：

Transfer-Learning; VGG; ICD; dance classification; Multi-Class problem;

D O I：

10.1109/WISPNET51692.2021.9419426

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Indian classical dance often referred to as ICD acts as a mirror of the rich cultural heritage of India. Every dance comes from a different state in the country. The national level academy for performing arts in India called The Sangeet Natak Academy identifies eight different dance forms namely- 'Bharatanatyam', 'Kathak', 'Kathakali', 'Kuchipudi', 'Manipuri', 'Mohiniyattam', 'Odissi' and 'Sattriya'. In this paper, we have used transfer learning to solve this multiclass problem. Pre-trained models such as VGG16 and VGG19 were used to classify the images of the 8 dance forms known in India. This was completed using the ICD data set from Kaggle consisting of 8 different classes and multiple images in each class.

引用

页码：278 / 282

页数：5

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