Recognition of OBIC's Variants by Using Deep Neural Networks and Spectral Clustering

人工智能 计算机科学 卷积神经网络 模式识别(心理学) 甲骨文公司 聚类分析 试验装置 集合(抽象数据类型) 人工神经网络 软件工程 程序设计语言
作者
Guoying Liu,Wenying Ge,Bingxin Du
标识
DOI:10.1109/iciscae52414.2021.9590692
摘要

Oracle bone inscriptions (OBIs) are the origin of Chinese characters and play a pivotal role in the study of Chinese civilization and the world civilization. The automatic recognition of OBI character (OBIC) images is very import to the research and promotion of OBI culture. However, a large amount of these ancient characters have variants with totally different appearance, which brings very serious negative impact on the OBI studies. In this paper, we proposed to recognize variants of OBICs by combining deep convolutional neural networks (DCNNs) with spectral clustering (SC). The former is employed to provide accurate descriptions for OBIC images, and the latter is used to find variants of each OBIC class. More specifically, the pretrained ResNet50 is exploited to obtain image features, and the normalized graph cuts is employed to find variants. Besides, a label propagation algorithm is used to find the label of test OBICs based on the clustering results. The proposed method is tested on an OBIC image set, in which all images are cropped from OBI rubbing images. Experimental results have shown that our method has the ability to recognize OBIC's variants.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
matilda完成签到 ,获得积分10
1秒前
1秒前
1秒前
汉堡包应助科研通管家采纳,获得10
1秒前
栗子应助科研通管家采纳,获得10
2秒前
坚强的哈密瓜完成签到,获得积分10
2秒前
猫会后空翻完成签到 ,获得积分10
2秒前
酷波er应助科研通管家采纳,获得10
2秒前
脑洞疼应助科研通管家采纳,获得10
2秒前
个性语堂应助科研通管家采纳,获得10
2秒前
yao应助科研通管家采纳,获得10
2秒前
烟花应助科研通管家采纳,获得10
2秒前
CodeCraft应助科研通管家采纳,获得10
2秒前
热气球应助科研通管家采纳,获得10
2秒前
乐乐应助科研通管家采纳,获得10
2秒前
香蕉觅云应助科研通管家采纳,获得10
2秒前
cdercder应助科研通管家采纳,获得30
2秒前
充电宝应助科研通管家采纳,获得10
2秒前
李爱国应助科研通管家采纳,获得10
2秒前
FashionBoy应助科研通管家采纳,获得10
2秒前
英俊的铭应助科研通管家采纳,获得10
2秒前
南北应助科研通管家采纳,获得10
2秒前
大刀开口完成签到,获得积分10
2秒前
Owen应助科研通管家采纳,获得10
2秒前
丘比特应助科研通管家采纳,获得10
2秒前
hzc应助科研通管家采纳,获得10
2秒前
充电宝应助科研通管家采纳,获得10
3秒前
3秒前
852应助科研通管家采纳,获得10
3秒前
咕咕发布了新的文献求助10
3秒前
violinsj完成签到,获得积分0
3秒前
ykyin发布了新的文献求助10
3秒前
nz发布了新的文献求助30
4秒前
Jrssion完成签到,获得积分10
5秒前
直率的钢铁侠完成签到,获得积分10
6秒前
MiSD完成签到,获得积分10
7秒前
大刀开口发布了新的文献求助30
8秒前
wj18637196763完成签到,获得积分10
9秒前
香蕉觅云应助zxj采纳,获得10
9秒前
9秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Les Mantodea de Guyane Insecta, Polyneoptera 1000
Structural Load Modelling and Combination for Performance and Safety Evaluation 1000
Conference Record, IAS Annual Meeting 1977 820
電気学会論文誌D(産業応用部門誌), 141 巻, 11 号 510
Typology of Conditional Constructions 500
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3571662
求助须知:如何正确求助?哪些是违规求助? 3142176
关于积分的说明 9445995
捐赠科研通 2843587
什么是DOI,文献DOI怎么找? 1562944
邀请新用户注册赠送积分活动 731458
科研通“疑难数据库(出版商)”最低求助积分说明 718551