DPVis: Automatic Visual Encoding Based on Deep Learning
Automatic visual encoding is frequently employed in automatic visualization tools to automatically map data to visual elements. This paper proposed an automatic visual encoding approach based on deep learning. This approach constructs visual encoding datasets in a more comprehensive and reliable man...
| Published in: | IEEE Access |
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| Main Authors: | , , , , , , , |
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10110957/ |
| _version_ | 1852653868738936832 |
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| author | Jia Liu Gang Wan Panke Deng Chu Li Shuai Wang Yunxia Yin Lei Liu Yao Mu |
| author_facet | Jia Liu Gang Wan Panke Deng Chu Li Shuai Wang Yunxia Yin Lei Liu Yao Mu |
| author_sort | Jia Liu |
| collection | DOAJ |
| container_title | IEEE Access |
| description | Automatic visual encoding is frequently employed in automatic visualization tools to automatically map data to visual elements. This paper proposed an automatic visual encoding approach based on deep learning. This approach constructs visual encoding datasets in a more comprehensive and reliable manner to extract and label widely available visualization graphics on the Internet in accordance with three essentials of visualization. The deep learning model is then trained to create a visual encoding model with powerful generalization performance, enabling automated effective visual encoding recommendations for visual designers. The results demonstrated that our approach extends the automatic visual encoding techniques used by existing visualization tools, enhances the functionality and performance of visualization tools, uncovers previously undiscovered data and increases the coverage of data variables. |
| format | Article |
| id | doaj-art-e15519ed413341dfb61ef92dbc1cd696 |
| institution | Directory of Open Access Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| spelling | doaj-art-e15519ed413341dfb61ef92dbc1cd6962025-08-19T21:39:53ZengIEEEIEEE Access2169-35362023-01-011111807811808710.1109/ACCESS.2023.327139310110957DPVis: Automatic Visual Encoding Based on Deep LearningJia Liu0https://orcid.org/0000-0002-4544-7434Gang Wan1Panke Deng2Chu Li3Shuai Wang4Yunxia Yin5Lei Liu6https://orcid.org/0000-0002-5849-9997Yao Mu7School of Space Information, Space Engineering University, Beijing, ChinaSchool of Space Information, Space Engineering University, Beijing, ChinaSchool of Space Information, Space Engineering University, Beijing, ChinaSchool of Space Information, Space Engineering University, Beijing, ChinaSchool of Space Information, Space Engineering University, Beijing, ChinaSchool of Space Information, Space Engineering University, Beijing, ChinaSchool of Space Information, Space Engineering University, Beijing, ChinaSchool of Space Information, Space Engineering University, Beijing, ChinaAutomatic visual encoding is frequently employed in automatic visualization tools to automatically map data to visual elements. This paper proposed an automatic visual encoding approach based on deep learning. This approach constructs visual encoding datasets in a more comprehensive and reliable manner to extract and label widely available visualization graphics on the Internet in accordance with three essentials of visualization. The deep learning model is then trained to create a visual encoding model with powerful generalization performance, enabling automated effective visual encoding recommendations for visual designers. The results demonstrated that our approach extends the automatic visual encoding techniques used by existing visualization tools, enhances the functionality and performance of visualization tools, uncovers previously undiscovered data and increases the coverage of data variables.https://ieeexplore.ieee.org/document/10110957/Automatic visualizationvisual encodingdeep learningvisual channels |
| spellingShingle | Jia Liu Gang Wan Panke Deng Chu Li Shuai Wang Yunxia Yin Lei Liu Yao Mu DPVis: Automatic Visual Encoding Based on Deep Learning Automatic visualization visual encoding deep learning visual channels |
| title | DPVis: Automatic Visual Encoding Based on Deep Learning |
| title_full | DPVis: Automatic Visual Encoding Based on Deep Learning |
| title_fullStr | DPVis: Automatic Visual Encoding Based on Deep Learning |
| title_full_unstemmed | DPVis: Automatic Visual Encoding Based on Deep Learning |
| title_short | DPVis: Automatic Visual Encoding Based on Deep Learning |
| title_sort | dpvis automatic visual encoding based on deep learning |
| topic | Automatic visualization visual encoding deep learning visual channels |
| url | https://ieeexplore.ieee.org/document/10110957/ |
| work_keys_str_mv | AT jialiu dpvisautomaticvisualencodingbasedondeeplearning AT gangwan dpvisautomaticvisualencodingbasedondeeplearning AT pankedeng dpvisautomaticvisualencodingbasedondeeplearning AT chuli dpvisautomaticvisualencodingbasedondeeplearning AT shuaiwang dpvisautomaticvisualencodingbasedondeeplearning AT yunxiayin dpvisautomaticvisualencodingbasedondeeplearning AT leiliu dpvisautomaticvisualencodingbasedondeeplearning AT yaomu dpvisautomaticvisualencodingbasedondeeplearning |