Sonu Kumar, Arjun Tyagi, Tarpit Sahu, Pushkar Shukla, A. Mittal
{"title":"使用卷积神经网络识别印度艺术形式","authors":"Sonu Kumar, Arjun Tyagi, Tarpit Sahu, Pushkar Shukla, A. Mittal","doi":"10.1109/SPIN.2018.8474290","DOIUrl":null,"url":null,"abstract":"Indian Culture is one of the richest cultures of the world. Art forms are a key component of the Indian culture that reveal a great deal about the various traditions, customs and practices of ancient as well as modern India. The paper proposes a framework classify Indian art forms into 8 different categories viz. Kalamkari, Kangra, Madhubani, Mural, Pattachitra, Portrait, Tanjore and Warli depending upon the style of art form. The proposed framework relies on the fusion of several state of the art deep convolutional neural networks for feature extraction from the dataset. Further, the experiments were carried out on a newly introduced dataset of over two thousand digital images of Indian paintings. The data-set is publically available for further experimentations. The model is able to achieve an accuracy of 86.56% outperforming other models.","PeriodicalId":184596,"journal":{"name":"2018 5th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Indian Art Form Recognition Using Convolutional Neural Networks\",\"authors\":\"Sonu Kumar, Arjun Tyagi, Tarpit Sahu, Pushkar Shukla, A. Mittal\",\"doi\":\"10.1109/SPIN.2018.8474290\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Indian Culture is one of the richest cultures of the world. Art forms are a key component of the Indian culture that reveal a great deal about the various traditions, customs and practices of ancient as well as modern India. The paper proposes a framework classify Indian art forms into 8 different categories viz. Kalamkari, Kangra, Madhubani, Mural, Pattachitra, Portrait, Tanjore and Warli depending upon the style of art form. The proposed framework relies on the fusion of several state of the art deep convolutional neural networks for feature extraction from the dataset. Further, the experiments were carried out on a newly introduced dataset of over two thousand digital images of Indian paintings. The data-set is publically available for further experimentations. The model is able to achieve an accuracy of 86.56% outperforming other models.\",\"PeriodicalId\":184596,\"journal\":{\"name\":\"2018 5th International Conference on Signal Processing and Integrated Networks (SPIN)\",\"volume\":\"36 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 5th International Conference on Signal Processing and Integrated Networks (SPIN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SPIN.2018.8474290\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 5th International Conference on Signal Processing and Integrated Networks (SPIN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPIN.2018.8474290","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Indian Art Form Recognition Using Convolutional Neural Networks
Indian Culture is one of the richest cultures of the world. Art forms are a key component of the Indian culture that reveal a great deal about the various traditions, customs and practices of ancient as well as modern India. The paper proposes a framework classify Indian art forms into 8 different categories viz. Kalamkari, Kangra, Madhubani, Mural, Pattachitra, Portrait, Tanjore and Warli depending upon the style of art form. The proposed framework relies on the fusion of several state of the art deep convolutional neural networks for feature extraction from the dataset. Further, the experiments were carried out on a newly introduced dataset of over two thousand digital images of Indian paintings. The data-set is publically available for further experimentations. The model is able to achieve an accuracy of 86.56% outperforming other models.