Analysis of Painting Elements of Tea Culture and Art Works Based on Image Perception
The art works take tea as the theme of painting elements, and depict the content of Chinese traditional culture by showing the connotation of tea culture painting elements. For example, Wen Zhiming, a painter and calligrapher in Ming Dynasty, is best at painting landscape figures. He often uses tea...
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description | The art works take tea as the theme of painting elements, and depict the content of Chinese traditional culture by showing the connotation of tea culture painting elements. For example, Wen Zhiming, a painter and calligrapher in Ming Dynasty, is best at painting landscape figures. He often uses tea as the theme in his art works, adding the elegant, natural, and simple painting elements of tea into his works, and has created many famous art works that are spread around the world. In recent years, with the deepening of the research on image processing technology, image perception technology has attracted more and more researchers’ attention, which has made great achievements and progress in image perception algorithms. This paper studies the tea culture elements of art works based on image perception algorithm. The accuracy of similar image pairs is the ratio that similar image pairs are correctly recognized. When the number of experiments reaches 25, the image sensing algorithm in this paper has the highest accuracy rate, with the accuracy rate of 99.8%, when the number of experiments reaches 20, the ant colony algorithm with the accuracy rate of 96.5%, when the number of experiments reaches 20, and finally the artificial intelligence algorithm with the accuracy rate of 96.0%. The correct rate of similar image pairs is the correct recognition rate of tampered image pairs. When the number of experiments reaches 10 times, the correct rate of image perception algorithm in this paper still ranks first, with the correct rate of 99.8%, when the number of experiments reaches 25 times, the artificial intelligence algorithm with the correct rate of 98.2%, and finally when the number of experiments reaches 10 times, the ant colony algorithm with the correct rate of 96.2%. It can be seen that the numerical values in this column show the robustness of this algorithm. Through the cultivation of students’ image perception ability, it can promote students’ appreciation ability of tea culture painting elements, learn the knowledge of tea culture painting elements beauty in a deeper level, master the skills of beauty, and improve their artistic temperament and artistic accomplishment. |
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For example, Wen Zhiming, a painter and calligrapher in Ming Dynasty, is best at painting landscape figures. He often uses tea as the theme in his art works, adding the elegant, natural, and simple painting elements of tea into his works, and has created many famous art works that are spread around the world. In recent years, with the deepening of the research on image processing technology, image perception technology has attracted more and more researchers’ attention, which has made great achievements and progress in image perception algorithms. This paper studies the tea culture elements of art works based on image perception algorithm. The accuracy of similar image pairs is the ratio that similar image pairs are correctly recognized. When the number of experiments reaches 25, the image sensing algorithm in this paper has the highest accuracy rate, with the accuracy rate of 99.8%, when the number of experiments reaches 20, the ant colony algorithm with the accuracy rate of 96.5%, when the number of experiments reaches 20, and finally the artificial intelligence algorithm with the accuracy rate of 96.0%. The correct rate of similar image pairs is the correct recognition rate of tampered image pairs. When the number of experiments reaches 10 times, the correct rate of image perception algorithm in this paper still ranks first, with the correct rate of 99.8%, when the number of experiments reaches 25 times, the artificial intelligence algorithm with the correct rate of 98.2%, and finally when the number of experiments reaches 10 times, the ant colony algorithm with the correct rate of 96.2%. It can be seen that the numerical values in this column show the robustness of this algorithm. Through the cultivation of students’ image perception ability, it can promote students’ appreciation ability of tea culture painting elements, learn the knowledge of tea culture painting elements beauty in a deeper level, master the skills of beauty, and improve their artistic temperament and artistic accomplishment.</description><identifier>ISSN: 1939-0114</identifier><identifier>EISSN: 1939-0122</identifier><identifier>DOI: 10.1155/2022/2439464</identifier><language>eng</language><publisher>London: Hindawi</publisher><subject>Algorithms ; Ant colony optimization ; Art works ; Artificial intelligence ; Artists ; Asian history ; Chinese history ; Image manipulation ; Image processing ; Image retrieval ; Object recognition ; Painters ; Perception ; Robustness (mathematics) ; Students</subject><ispartof>Security and communication networks, 2022-07, Vol.2022, p.1-9</ispartof><rights>Copyright © 2022 Haiting Zhao.</rights><rights>Copyright © 2022 Haiting Zhao. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c337t-6b71191ecf51a8b16741b1b9d836055fbefc34bc355ba6395b25630a050e507f3</citedby><cites>FETCH-LOGICAL-c337t-6b71191ecf51a8b16741b1b9d836055fbefc34bc355ba6395b25630a050e507f3</cites><orcidid>0000-0001-9183-3478</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><contributor>Liu, Jun</contributor><contributor>Jun Liu</contributor><creatorcontrib>Zhao, Haiting</creatorcontrib><title>Analysis of Painting Elements of Tea Culture and Art Works Based on Image Perception</title><title>Security and communication networks</title><description>The art works take tea as the theme of painting elements, and depict the content of Chinese traditional culture by showing the connotation of tea culture painting elements. For example, Wen Zhiming, a painter and calligrapher in Ming Dynasty, is best at painting landscape figures. He often uses tea as the theme in his art works, adding the elegant, natural, and simple painting elements of tea into his works, and has created many famous art works that are spread around the world. In recent years, with the deepening of the research on image processing technology, image perception technology has attracted more and more researchers’ attention, which has made great achievements and progress in image perception algorithms. This paper studies the tea culture elements of art works based on image perception algorithm. The accuracy of similar image pairs is the ratio that similar image pairs are correctly recognized. When the number of experiments reaches 25, the image sensing algorithm in this paper has the highest accuracy rate, with the accuracy rate of 99.8%, when the number of experiments reaches 20, the ant colony algorithm with the accuracy rate of 96.5%, when the number of experiments reaches 20, and finally the artificial intelligence algorithm with the accuracy rate of 96.0%. The correct rate of similar image pairs is the correct recognition rate of tampered image pairs. When the number of experiments reaches 10 times, the correct rate of image perception algorithm in this paper still ranks first, with the correct rate of 99.8%, when the number of experiments reaches 25 times, the artificial intelligence algorithm with the correct rate of 98.2%, and finally when the number of experiments reaches 10 times, the ant colony algorithm with the correct rate of 96.2%. It can be seen that the numerical values in this column show the robustness of this algorithm. Through the cultivation of students’ image perception ability, it can promote students’ appreciation ability of tea culture painting elements, learn the knowledge of tea culture painting elements beauty in a deeper level, master the skills of beauty, and improve their artistic temperament and artistic accomplishment.</description><subject>Algorithms</subject><subject>Ant colony optimization</subject><subject>Art works</subject><subject>Artificial intelligence</subject><subject>Artists</subject><subject>Asian history</subject><subject>Chinese history</subject><subject>Image manipulation</subject><subject>Image processing</subject><subject>Image retrieval</subject><subject>Object recognition</subject><subject>Painters</subject><subject>Perception</subject><subject>Robustness 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Haiting</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c337t-6b71191ecf51a8b16741b1b9d836055fbefc34bc355ba6395b25630a050e507f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Ant colony optimization</topic><topic>Art works</topic><topic>Artificial intelligence</topic><topic>Artists</topic><topic>Asian history</topic><topic>Chinese history</topic><topic>Image manipulation</topic><topic>Image processing</topic><topic>Image retrieval</topic><topic>Object recognition</topic><topic>Painters</topic><topic>Perception</topic><topic>Robustness (mathematics)</topic><topic>Students</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhao, Haiting</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open 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Elements of Tea Culture and Art Works Based on Image Perception</atitle><jtitle>Security and communication networks</jtitle><date>2022-07-12</date><risdate>2022</risdate><volume>2022</volume><spage>1</spage><epage>9</epage><pages>1-9</pages><issn>1939-0114</issn><eissn>1939-0122</eissn><abstract>The art works take tea as the theme of painting elements, and depict the content of Chinese traditional culture by showing the connotation of tea culture painting elements. For example, Wen Zhiming, a painter and calligrapher in Ming Dynasty, is best at painting landscape figures. He often uses tea as the theme in his art works, adding the elegant, natural, and simple painting elements of tea into his works, and has created many famous art works that are spread around the world. In recent years, with the deepening of the research on image processing technology, image perception technology has attracted more and more researchers’ attention, which has made great achievements and progress in image perception algorithms. This paper studies the tea culture elements of art works based on image perception algorithm. The accuracy of similar image pairs is the ratio that similar image pairs are correctly recognized. When the number of experiments reaches 25, the image sensing algorithm in this paper has the highest accuracy rate, with the accuracy rate of 99.8%, when the number of experiments reaches 20, the ant colony algorithm with the accuracy rate of 96.5%, when the number of experiments reaches 20, and finally the artificial intelligence algorithm with the accuracy rate of 96.0%. The correct rate of similar image pairs is the correct recognition rate of tampered image pairs. When the number of experiments reaches 10 times, the correct rate of image perception algorithm in this paper still ranks first, with the correct rate of 99.8%, when the number of experiments reaches 25 times, the artificial intelligence algorithm with the correct rate of 98.2%, and finally when the number of experiments reaches 10 times, the ant colony algorithm with the correct rate of 96.2%. It can be seen that the numerical values in this column show the robustness of this algorithm. Through the cultivation of students’ image perception ability, it can promote students’ appreciation ability of tea culture painting elements, learn the knowledge of tea culture painting elements beauty in a deeper level, master the skills of beauty, and improve their artistic temperament and artistic accomplishment.</abstract><cop>London</cop><pub>Hindawi</pub><doi>10.1155/2022/2439464</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0001-9183-3478</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Ant colony optimization Art works Artificial intelligence Artists Asian history Chinese history Image manipulation Image processing Image retrieval Object recognition Painters Perception Robustness (mathematics) Students |
title | Analysis of Painting Elements of Tea Culture and Art Works Based on Image Perception |
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