DPVis: Automatic Visual Encoding Based on Deep Learning

• Published in: IEEE access Vol. 11; p. 1
• Main Authors: Liu, Jia, Wan, Gang, Deng, Panke, Li, Chu, Wang, Shuai, Yin, Yunxia, Liu, Lei, Mu, Yao
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Automatic visualization; Coding; Data models; Data visualization; Deep learning; Encoding; Graphics; Task analysis; visual channels; visual encoding; Visualization
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3271393

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 dataset 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.