ReVision

Savva, Manolis; Kong, Nicholas; Chhajta, Arti; Li, Feifei; Agrawala, Maneesh; Heer, Jeffrey

doi:10.1145/2047196.2047247

Cited by 211 publications

(56 citation statements)

References 30 publications

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“…Most of these methods use older saliency architectures based on hand-crafted features that are inferior to the state-of-the-art neural networks we use in our approach. Our work also relates to the general program of applying computer vision and machine learning in the service of graphic design tools [27,28,42].…”

Section: Related Workmentioning

confidence: 99%

Learning Visual Importance for Graphic Designs and Data Visualizations

Bylinskii

Kim

O’Donovan

et al. 2017

Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology

145

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Knowing where people look and click on visual designs can provide clues about how the designs are perceived, and where the most important or relevant content lies. The most important content of a visual design can be used for effective summarization or to facilitate retrieval from a database. We present automated models that predict the relative importance of different elements in data visualizations and graphic designs. Our models are neural networks trained on human clicks and importance annotations on hundreds of designs. We collected a new dataset of crowdsourced importance, and analyzed the predictions of our models with respect to ground truth importance and human eye movements. We demonstrate how such predictions of importance can be used for automatic design retargeting and thumbnailing. User studies with hundreds of MTurk participants validate that, with limited post-processing, our importance-driven applications are on par with, or outperform, current state-of-the-art methods, including natural image saliency. We also provide a demonstration of how our importance predictions can be built into interactive design tools to offer immediate feedback during the design process

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Section: Related Workmentioning

confidence: 99%

Learning Visual Importance for Graphic Designs and Data Visualizations

Bylinskii

Kim

O’Donovan

et al. 2017

Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology

145

View full text Add to dashboard Cite

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“…In this case an image of the visualization can be captured subsequent to every interaction. The images can be processed using computer vision and machine learning algorithms to extract the properties, such as axis labels or data items [17,26,27,34]. The extracted properties can form a visualization state and/or list of retrieval-relevant properties that are served as input for our retrieval approach (see Figure 5b).…”

Section: Discussion and Limitations 81 Generalizabilitymentioning

confidence: 99%

KnowledgePearls: Provenance-Based Visualization Retrieval

Stitz

Gratzl

Piringer

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Storing analytical provenance generates a knowledge base with a large potential for recalling previous results and guiding users in future analyses. However, without extensive manual creation of meta information and annotations by the users, search and retrieval of analysis states can become tedious. We present KnowledgePearls, a solution for efficient retrieval of analysis states that are structured as provenance graphs containing automatically recorded user interactions and visualizations. As a core component, we describe a visual interface for querying and exploring analysis states based on their similarity to a partial definition of a requested analysis state. Depending on the use case, this definition may be provided explicitly by the user by formulating a search query or inferred from given reference states. We explain our approach using the example of efficient retrieval of demographic analyses by Hans Rosling and discuss our implementation for a fast look-up of previous states. Our approach is independent of the underlying visualization framework. We discuss the applicability for visualizations which are based on the declarative grammar Vega and we use a Vega-based implementation of Gapminder as guiding example. We additionally present a biomedical case study to illustrate how KnowledgePearls facilitates the exploration process by recalling states from earlier analyses.

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“…This approach involves the use of image manipulation and computer vision techniques to support the semi-guided extraction and manipulation of visual elements. Computer vision methods have been previously used in visualization, though for different purposes such as automatically extracting and retargeting colour mappings [41,34], and visualization encodings [33,28] from raster images. Image manipulations have also been applied in visualization, for instance in Transmogrifiers [13] to distort regions of an image into more meaningful visualizations.…”

Section: Visualization and Image Processingmentioning

confidence: 99%

DataQuilt: Extracting Visual Elements from Images to Craft Pictorial Visualizations

Zhang

Sultanum

Bezerianos

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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Figure 1. Examples of creations realized with DataQuilt, a new interactive authoring tool that allows authors to borrow visual and stylistic elements from raster images and re-purpose them to create custom, pictorial visualizations. Left: a scatterplot of famous paintings by Klimt, showing the date of creation (x-axis) against how much it was sold for in auction (y-axis). Each data point is a spiral-shaped glyph whose texture is mapped to the painting it represents, whereas the Tree of Life painting is used as a decorative background. Middle: a bar chart representing the distance from the sun for the planets of our solar system. Each data point is represented by a space rocket, whose exhaust flames are stretched according to the underlying data. Decorative glyphs (sun, planets) are used for further information and visual appeal. Right: A personal visualization depicting one's coffee intake over a week. The type of coffee (espresso, latte, etc.) is represented by different coffee cups, all extracted from photographs. The orientation of the handle represents the time, whereas size is proportional to the drink size and horizontal position corresponds to the day of the week.

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ReVision

Cited by 211 publications

References 30 publications

Learning Visual Importance for Graphic Designs and Data Visualizations

Learning Visual Importance for Graphic Designs and Data Visualizations

KnowledgePearls: Provenance-Based Visualization Retrieval

DataQuilt: Extracting Visual Elements from Images to Craft Pictorial Visualizations

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