InfoColorizer: Interactive Recommendation of Color Palettes for Infographics

Wang²,

IEEE Trans. Visual. Comput. Graphics

2024

In recent years, narrative visualization has gained much attention. Researchers have proposed different design spaces for various narrative visualization genres and scenarios to facilitate the creation process. As users' needs grow and automation technologies advance, increasingly more tools have been designed and developed. In this study, we summarized six genres of narrative visualization (annotated charts, infographics, timelines & storylines, data comics, scrollytelling & slideshow, and data videos) based on previous research and four types of tools (design spaces, authoring tools, ML/AI-supported tools and ML/AI-generator tools) based on the intelligence and automation level of the tools. We surveyed 105 papers and tools to study how automation can progressively engage in visualization design and narrative processes to help users easily create narrative visualizations. This research aims to provide an overview of current research and development in the automation involvement of narrative visualization tools. We discuss key research problems in each category and suggest new opportunities to encourage further research in the related domain.

Section: Infographicmentioning

confidence: 99%

How Does Automation Shape the Process of Narrative Visualization: A Survey of Tools

Wang²,

IEEE Trans. Visual. Comput. Graphics

2024

“…One compelling application is to retrieve values and labels from charts [38,40,66,75,82,97] to improve visualization design [91] or perform Visual Question Answering (VQA) [39,83,84]. Deep neural networks were used to extract features from infographics [59] to automate design processes [11,14], generate recommendations [32,54,61,118,121], and highlight visual salience [10]. Alternatively, visualizations are effective means to explain and interpret a neural network (see [30] for a survey).…”

Section: Machine Learning and Visualizationmentioning

confidence: 99%

How Can Deep Neural Networks Aid Visualization Perception Research?

Yang¹,

Ma²,

Harrison³

et al. 2023

Preprint

How deep neural networks can aid visualization perception research is a wide-open question. This paper provides insights from three perspectives—prediction, generalization, and interpretation— via training and analyzing deep convolutional neural networks on human correlation judgments in scatterplots across three studies. The first study assesses the accuracy of twenty-nine neural network architectures in predicting human judgments, _nding that a subset of the architectures (e.g., VGG-19) has comparable accuracy to the best-performing regression analyses used in prior research. The second study shows that the resulting models from the first study display better generalizability than prior models on two other judgment datasets for different scatterplot designs. The third study interprets visual features learned by a convolutional neural network model, providing insights about how the model makes predictions, and identifies new features that may play a role in human correlation perception. Together, this paper suggests that deep neural networks can serve as a potential tool for aiding empirical studies on visualization perception, and as a potential means for informing automatic visualization design and evaluation.

“…In addition, the colors of an infographic largely affect the audience's primary impression [39], [44]. InfoColorizer [57] allows users to invoke color palettes when creating data-driven infographics. Users can try out different infographic layouts and get corresponding palette recommendations to refine their designs.…”

Section: Infographicmentioning

confidence: 99%

“…The third direction is to enhance research on intelligent algorithms. Many existing algorithms are still rule-based (e.g., icon selection, color selection [57], etc). More advanced machine-learning techniques can to be applied to improve the quality of infographics generated by visualization systems and make them adaptable to different users in various domains.…”

Section: Infographicmentioning

confidence: 99%

How Does Automation Shape the Process of Narrative Visualization: A Survey on Tools

Chen¹,

Cao²,

Wang³

et al. 2022

Preprint

In recent years, narrative visualization has gained a lot of attention. Researchers have proposed different design spaces for various narrative visualization types and scenarios to facilitate the creation process. As users' needs grow and automation technologies advance, more and more tools have been designed and developed. In this paper, we surveyed 122 papers and tools to study how automation can progressively engage in the visualization design and narrative process. By investigating the narrative strengths and the drawing efforts of various visualizations, we created a two-dimensional coordinate to map different visualization types. Our resulting taxonomy is organized by the seven types of narrative visualization on the +x-axis of the coordinate and the four automation levels (i.e., design space, authoring tool, AI-supported tool, and AI-generator tool) we identified from the collected work. The taxonomy aims to provide an overview of current research and development in the automation involvement of narrative visualization tools. We discuss key research problems in each category and suggest new opportunities to encourage further research in the related domain.