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DataQuilt: Extracting Visual Elements from Images to Craft Pictorial Visualizations

Abstract : Recent years have seen an increasing interest in the authoring and crafting of personal visualizations. Mainstream data analysis and authoring tools lack the flexibility for customization and personalization, whereas tools from the research community either require creativity and drawing skills, or are limited to simple vector graphics. We present DataQuilt, a novel system that enables visualization authors to iteratively design pictorial visualizations as collages. Real images (e.g., paintings, photographs, sketches) act as both inspiration and as a resource of visual elements that can be mapped to data. The creative pipeline involves the semi-guided extraction of relevant elements of an image (arbitrary regions, regular shapes, color palettes, textures) aided by computer vision techniques; the binding of these graphical elements and their features to data in order to create meaningful visualizations; and the iterative refinement of both features and visualizations through direct manipulation. We demonstrate the usability of DataQuilt in a controlled study and its expressiveness through a collection of authored visualizations from a second open-ended study.
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Submitted on : Monday, May 4, 2020 - 12:10:35 PM
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Jiayi Eris Zhang, Nicole Sultanum, Anastasia Bezerianos, Fanny Chevalier. DataQuilt: Extracting Visual Elements from Images to Craft Pictorial Visualizations. CHI '20 - 38th SIGCHI conference on Human Factors in computing systems, ACM, Apr 2020, Honolulu, United States. ⟨10.1145/3313831.3376172⟩. ⟨hal-02562015⟩



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