TY - CPAPER
KW - Baseline results
KW - Benchmark dataset
KW - Benchmark datasets
KW - Blue calico
KW - Classification (of information)
KW - Computer Vision
KW - Convolutional neural networks
KW - Deep learning
KW - Image datasets
KW - Image recognition
KW - Intangible cultural heritage
KW - Intangible cultural heritages
KW - Large dataset
KW - Large-scale datasets
KW - Learning systems
KW - Nantong
KW - Public dataset
KW - Public image
AU - Xiang Yu
AU - Li Zhang
AU - Mei Shen
AU - IEEE
AB - Nantong blue calico is a kind of important intangible cultural heritages in China. To better safeguard and inherit it in a digital way, it is necessary to construct a large-scale dataset for Nantong blue calico. As so far, however, we could not find a public dataset for blue calico. The goal of this paper is to give a public image dataset which named N tBC consisting of Nantong blue calico patterns and provide a baseline result for the recognition of Nantong blue calico patterns. In this paper, we perform several baseline experiments on the NtBC dataset, including handcrafted and deep feature based classification methods. we compare some handcrafted methods and four kinds of popular convolutional neural networks (CNNs), including ResNet-50, AlexNet, GoogLeNet-V1 and VGGNet-16. Experimental results show that ResNet-50 yields an accuracy of 93.8\% in the recognition performance, which shows that it is efficient to classify blue calico patterns through deep learning methods. As a consequence, this result provides the current best baseline result for Nantong blue calico image recognition. We believe our N tBC will facilitate future research on Chinese traditional patterns development, fine grained visual classification, and imbalanced learning fields. We make the dataset and pre-trained models publicly available at https://github.com/facebook/react.
C2 - IEEE Congr. Evol. Comput., CEC - Conf. Proc.
DO - 10.1109/CEC55065.2022.9870225
N1 - Journal Abbreviation: IEEE Congr. Evol. Comput., CEC - Conf. Proc.
N2 - Nantong blue calico is a kind of important intangible cultural heritages in China. To better safeguard and inherit it in a digital way, it is necessary to construct a large-scale dataset for Nantong blue calico. As so far, however, we could not find a public dataset for blue calico. The goal of this paper is to give a public image dataset which named N tBC consisting of Nantong blue calico patterns and provide a baseline result for the recognition of Nantong blue calico patterns. In this paper, we perform several baseline experiments on the NtBC dataset, including handcrafted and deep feature based classification methods. we compare some handcrafted methods and four kinds of popular convolutional neural networks (CNNs), including ResNet-50, AlexNet, GoogLeNet-V1 and VGGNet-16. Experimental results show that ResNet-50 yields an accuracy of 93.8\% in the recognition performance, which shows that it is efficient to classify blue calico patterns through deep learning methods. As a consequence, this result provides the current best baseline result for Nantong blue calico image recognition. We believe our N tBC will facilitate future research on Chinese traditional patterns development, fine grained visual classification, and imbalanced learning fields. We make the dataset and pre-trained models publicly available at https://github.com/facebook/react.
PB - Institute of Electrical and Electronics Engineers Inc.
SN - 9781665467087 (ISBN)
TI - Nantong Blue Calico Image Dataset and Its Recognition
UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138719484&doi=10.1109%2fCEC55065.2022.9870225&partnerID=40&md5=8952d911d139094354d4ac20d84c2093
ER -