CASIA OpenIR  > 毕业生  > 硕士学位论文
Thesis Advisor杨青
Degree Grantor中国科学院大学
Place of Conferral中国科学院自动化研究所
Degree Name工学硕士
Degree Discipline模式识别与智能系统
Keyword甲骨文字识别 深度度量学习 跨模态 最近邻识别


       提出了一种基于深度度量学习的临摹甲骨文识别算法,有效克服了临摹甲骨文字样本量不足、样本类别不均衡以及样本类内差异过大的问题。首先基于卷积神经网络(Convolutional Nerual Network, CNN)将临摹甲骨文字图像映射到一个特征空间,使得特征间的欧式距离可以衡量对应的临摹甲骨文字图像间的差异。继而在该空间内基于最近邻(Nearest Neighbor,NN)法则进行识别。本文提出的算法在已知类分类任务上验证了优于现有算法,同时在未知类别拒识、开放集识别等多个任务上验证了算法的有效性。最后,本文提出了一种原型裁剪算法,有效地缓解了最近邻分类速度慢的问题。


Other Abstract

    Oracle character is one kind of the earliest hieroglyphics, which can be dated back to Shang Dynasty in China. Oracle character recognition is important for modern archaeology, ancient text understanding, historical chronology, etc. There are two kinds of oracle character images, one is Linmo Oracle Character (LOC) images and the other is Tapian Oracle Character (TOC) images. TOC images are directly collected from oracle bones, after image denoising and restoration, it becomes LOC images. Our efforts and contributions are divided into two parts:

    To overcome the limitation and class imbalance of training data in LOC recognition, we propose a classi?cation method based on deep metric learning. A convolutional neural network (CNN) is used to map the LOC images to a feature space where the distance between different samples can measure their similarities such that classi?cation can be performed by the Nearest Neighbor (NN) rule. Experimental results show that the proposed method exceeds the existing ones in LOC recognition.

    For the recognition of TOC, a framework is proposed to improve the performance by taking advantage of LOC. We also use a CNN to map TOC to a feature space whose dimension is same as the one of LOC. Then, unsupervised domain adaptation is carried out in that feature space to make TOC have the same distribution with LOC. Moreover, the feature of TOC is further adjusted via deep metric learning in order to fulfill NN classification. Our proposed method not only achieves better performance in TOC recognition but also can figure out new categories.

Document Type学位论文
Corresponding Author张颐康
Recommended Citation
GB/T 7714
张颐康. 基于深度度量学习的甲骨文字识别[D]. 中国科学院自动化研究所. 中国科学院大学,2019.
Files in This Item:
File Name/Size DocType Version Access License
张颐康_学位论文.pdf(4167KB)学位论文 限制开放CC BY-NC-SA
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[张颐康]'s Articles
Baidu academic
Similar articles in Baidu academic
[张颐康]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[张颐康]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.