Ancient Chinese Character Image Retrieval Based on Dual Hesitant Fuzzy Sets
The complex and changeable structures of ancient Chinese characters result in the decreasing accuracy of their image retrieval. To resolve this problem, a new retrieval method based on dual hesitant fuzzy sets is proposed. Dual hesitation fuzzy sets that can express uncertain information more compre...
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2021-01-01
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Series: | Scientific Programming |
Online Access: | http://dx.doi.org/10.1155/2021/6621037 |
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doaj-c8b2738ea9ed43cc88af3c27d4e0888b2021-07-02T21:20:51ZengHindawi LimitedScientific Programming1875-919X2021-01-01202110.1155/2021/6621037Ancient Chinese Character Image Retrieval Based on Dual Hesitant Fuzzy SetsSongbo Du0Fang Yang1Xuedong Tian2School of Cyber Security and ComputerSchool of Cyber Security and ComputerSchool of Cyber Security and ComputerThe complex and changeable structures of ancient Chinese characters result in the decreasing accuracy of their image retrieval. To resolve this problem, a new retrieval method based on dual hesitant fuzzy sets is proposed. Dual hesitation fuzzy sets that can express uncertain information more comprehensively are employed in the feature extraction process of directional line elements. The multiattribute evaluation index of adjacent grids for the current grid and its corresponding membership and nonmembership functions are established, and the weight of each attribute is calculated by the dual hesitation fuzzy entropy, such that the proposed features can fully reflect the topological structure of ancient Chinese characters. Using the dual hesitation fuzzy correlation coefficient to measure the similarity between the ancient Chinese character images to be retrieved and the candidate images, the retrieval of ancient Chinese character images is realized. Experiments show that when the t0hreshold value of the correlation coefficient is 0.9, the average retrieval accuracy is 90.4%.http://dx.doi.org/10.1155/2021/6621037 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Songbo Du Fang Yang Xuedong Tian |
spellingShingle |
Songbo Du Fang Yang Xuedong Tian Ancient Chinese Character Image Retrieval Based on Dual Hesitant Fuzzy Sets Scientific Programming |
author_facet |
Songbo Du Fang Yang Xuedong Tian |
author_sort |
Songbo Du |
title |
Ancient Chinese Character Image Retrieval Based on Dual Hesitant Fuzzy Sets |
title_short |
Ancient Chinese Character Image Retrieval Based on Dual Hesitant Fuzzy Sets |
title_full |
Ancient Chinese Character Image Retrieval Based on Dual Hesitant Fuzzy Sets |
title_fullStr |
Ancient Chinese Character Image Retrieval Based on Dual Hesitant Fuzzy Sets |
title_full_unstemmed |
Ancient Chinese Character Image Retrieval Based on Dual Hesitant Fuzzy Sets |
title_sort |
ancient chinese character image retrieval based on dual hesitant fuzzy sets |
publisher |
Hindawi Limited |
series |
Scientific Programming |
issn |
1875-919X |
publishDate |
2021-01-01 |
description |
The complex and changeable structures of ancient Chinese characters result in the decreasing accuracy of their image retrieval. To resolve this problem, a new retrieval method based on dual hesitant fuzzy sets is proposed. Dual hesitation fuzzy sets that can express uncertain information more comprehensively are employed in the feature extraction process of directional line elements. The multiattribute evaluation index of adjacent grids for the current grid and its corresponding membership and nonmembership functions are established, and the weight of each attribute is calculated by the dual hesitation fuzzy entropy, such that the proposed features can fully reflect the topological structure of ancient Chinese characters. Using the dual hesitation fuzzy correlation coefficient to measure the similarity between the ancient Chinese character images to be retrieved and the candidate images, the retrieval of ancient Chinese character images is realized. Experiments show that when the t0hreshold value of the correlation coefficient is 0.9, the average retrieval accuracy is 90.4%. |
url |
http://dx.doi.org/10.1155/2021/6621037 |
work_keys_str_mv |
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1721322204750151680 |