Deep Colormap Extraction From Visualizations

Wen, Zikai Alex; Zeng, Wei; Fu, Siwei; Zeng, Zhiliang; Li, Haotian; Qu, Huamin

doi:10.1109/tvcg.2021.3070876

Cited by 19 publications

(4 citation statements)

References 57 publications

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“…Therefore, AIassisted tools have leveraged existing examples to guide colormap construction. For example, deep learning can enable colormap style transfer from existing images to new charts [37,64,76,77]. However, users may not have quality example images.…”

Section: Tools For Designing Sequential Colormapsmentioning

confidence: 99%

Cieran: Designing Sequential Colormaps via In-Situ Active Preference Learning

Hong,

Sunberg,

Szafir

2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Figure 1: Cieran's preference learning interface used with Altair [71]. Cieran supports efficient colormap selection within an analyst's workflow through integration with Jupyter Notebooks. Cieran first interpolates example colormaps through a chosen color (e.g., #186E8D, a teal blue shown in the progress bar). People iteratively input preferences to Cieran by making value judgements across pairs of examples, and Cieran uses the preference data to induce a context-specific model of aesthetic utility. This model used to rank and create colormaps.

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Section: Tools For Designing Sequential Colormapsmentioning

confidence: 99%

Cieran: Designing Sequential Colormaps via In-Situ Active Preference Learning

Hong,

Sunberg,

Szafir

2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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show abstract

“…Despite the importance, the design space of chart attributes is too large for designers to choose from, resulting in various types of charts in designers' own style [SLC * 23]. Most of these works follow a coarse‐to‐fine strategy to first identify chart types and then extract visual marks and channels [SKC * 11,JKS * 17,PMH17,YZF * 22].…”

Section: Related Workmentioning

confidence: 99%

WYTIWYR: A User Intent‐Aware Framework with Multi‐modal Inputs for Visualization Retrieval

Xiao

Hou

Cheng

et al. 2023

Computer Graphics Forum

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Retrieving charts from a large corpus is a fundamental task that can benefit numerous applications such as visualization recommendations. The retrieved results are expected to conform to both explicit visual attributes (e.g., chart type, colormap) and implicit user intents (e.g., design style, context information) that vary upon application scenarios. However, existing example‐based chart retrieval methods are built upon non‐decoupled and low‐level visual features that are hard to interpret, while definition‐based ones are constrained to pre‐defined attributes that are hard to extend. In this work, we propose a new framework, namely WYTIWYR (What‐You‐Think‐Is‐What‐You‐Retrieve), that integrates user intents into the chart retrieval process. The framework consists of two stages: first, the Annotation stage disentangles the visual attributes within the query chart; and second, the Retrieval stage embeds the user's intent with customized text prompt as well as bitmap query chart, to recall targeted retrieval result. We develop aprototype WYTIWYR system leveraging a contrastive language‐image pre‐training (CLIP) model to achieve zero‐shot classification as well as multi‐modal input encoding, and test the prototype on a large corpus with charts crawled from the Internet. Quantitative experiments, case studies, and qualitative interviews are conducted. The results demonstrate the usability and effectiveness of our proposed framework.

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“…Third, it can facilitate the construction of large-scale visualization corpora. Most of the existing visualization corpora only provide high-level labels of visualizations such as the visualization type [18] and color usage [55]. With our approach, fine-grained labels (e.g., the trend of data) of visualizations can be easily labeled based on similar visualization retrieval.…”

Section: Generalizability and Application Scenariosmentioning

confidence: 99%

Structure-aware Visualization Retrieval

Li,

Wang,

et al. 2022

Preprint

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With the wide usage of data visualizations, a huge number of Scalable Vector Graphic (SVG)-based visualizations have been created and shared online. Accordingly, there has been an increasing interest in exploring how to retrieve perceptually similar visualizations from a large corpus, since it can benefit various downstream applications such as visualization recommendation. Existing methods mainly focus on the visual appearance of visualizations by regarding them as bitmap images. However, the structural information intrinsically existing in SVG-based visualizations is ignored. Such structural information can delineate the spatial and hierarchical relationship among visual elements, and characterize visualizations thoroughly from a new perspective. This paper presents a structureaware method to advance the performance of visualization retrieval by collectively considering both the visual and structural information. We extensively evaluated our approach through quantitative comparisons, a user study and case studies. The results demonstrate the effectiveness of our approach and its advantages over existing methods.

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Deep Colormap Extraction From Visualizations

Cited by 19 publications

References 57 publications

Cieran: Designing Sequential Colormaps via In-Situ Active Preference Learning

Cieran: Designing Sequential Colormaps via In-Situ Active Preference Learning

WYTIWYR: A User Intent‐Aware Framework with Multi‐modal Inputs for Visualization Retrieval

Structure-aware Visualization Retrieval

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