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

doi:10.3969/j.issn.1000-1565.2020.06.014

Abstract

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

CLC Number:

TP391.43

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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