一种古籍汉字图像的多属性模糊检索模型

doi:10.3969/j.issn.1000-1565.2020.06.014

河北大学学报(自然科学版) ›› 2020, Vol. 40 ›› Issue (6): 657-665.DOI: 10.3969/j.issn.1000-1565.2020.06.014

一种古籍汉字图像的多属性模糊检索模型

齐艳媚¹,田学东¹,张充¹,李亚康²

收稿日期:2020-02-15 发布日期:2021-01-10
通讯作者: 国家自然科学基金资助项目(61375075);河北省自然科学基金资助项目(F2019201329);河北省教育厅河北省高等学校科学技术研究重点资助项目(ZD2017208)
作者简介:齐艳媚(1993—),女,河北沧州人,河北大学在读硕士研究生,主要从事模式识别研究. E-mail:704725966@qq.com

A multi-attribute fuzzy retrieval model for ancient Chinese charater images

QI Yanmei¹, TIAN Xuedong¹, ZHANG Chong¹, LI Yakang²

1. School of Cyber Security and Computer, Hebei University, Baoding 071002, China; 2. Center of Information, Affiliated Hospital of Hebei University, Baoding 071000, China

Received:2020-02-15 Published:2021-01-10

摘要/Abstract

摘要： 针对古籍汉字结构复杂、风格多变以及字形图像降质所导致传统文字图像检索技术在应用于古籍汉字图像时性能不佳的问题,引入犹豫模糊集理论,提出了一种古籍汉字图像检索模型.首先,设计面向古籍汉字图像的重叠模糊规范化双弹性网格划分,通过考察当前网格与其近邻网格间各种字形要素间的几何和统计特征,定义相应的犹豫模糊元素,进而构成古籍汉字查询图像和目标图像的犹豫模糊集合;其次,以犹豫模糊集合的加权距离测度作为古籍汉字查询图像和目标图像的相似性测度,得到古籍汉字图像检索结果的有序输出.本文算法在11 574幅古籍汉字图像上的检索查准率和查全率分别为78.9%和76.5%.

关键词: 古籍汉字图像, 图像检索, 犹豫模糊集, 多属性, 加权距离测度

Abstract: The traditional character image retrieval technology was difficult to meet the needs of users when it was applied to retrieve ancient Chinese character images because of the complex structure of ancient Chinese characters, changing of font styles, and degradation of font images. To address this problem, a multi-attribute fuzzy retrieval model of Chinese characters in ancient books was proposed by introducing the theory of hesitant fuzzy set. Firstly, an overlapping fuzzy normalized Bi elastic mesh generation for Chinese character images of ancient books was designed. A hesitant fuzzy set containing the query images and target images was established by investigating the geometric and statistical characteristics of various glyph elements between the current grid and its adjacent grids, and defining corresponding hesitant fuzzy elements. Secondly, the weighted distance measure of hesitant fuzzy sets was used to the similarity measure between the query image and the target image, then obtaining the orderly output of the retrieval results. The precision rate and recall rate of retrieval experiments on 11 574 ancient Chinese character images are 78.9% and 76.5%, respectively.

Key words: ancient Chinese character images, image retrieval, hesitant fuzzy set, multi attribute, weights distance measures

中图分类号:

TP391.43

齐艳媚,田学东,张充,李亚康. 一种古籍汉字图像的多属性模糊检索模型[J]. 河北大学学报(自然科学版), 2020, 40(6): 657-665.

QI Yanmei, TIAN Xuedong, ZHANG Chong, LI Yakang. A multi-attribute fuzzy retrieval model for ancient Chinese charater images[J]. Journal of Hebei University(Natural Science Edition), 2020, 40(6): 657-665.

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一种古籍汉字图像的多属性模糊检索模型

A multi-attribute fuzzy retrieval model for ancient Chinese charater images

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