Browsing by Author "Onzenoodt, Christian van"

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    Semantic Hierarchical Exploration of Large Image Datasets
    (The Eurographics Association, 2023) Bäuerle, Alex; Onzenoodt, Christian van; Jönsson, Daniel; Ropinski, Timo; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
    We present a method for exploring and comparing large sets of images with metadata using a hierarchical interaction approach. Browsing many images at the same time requires either a large screen space or an abundance of scrolling interaction. We address this problem by projecting the images onto a two-dimensional Cartesian coordinate system by combining the latent space of vision neural networks and dimensionality reduction techniques. To alleviate overdraw of the images, we integrate a hierarchical layout and navigation, where each group of similar images is represented by the image closest to the group center. Advanced interactive analysis of images in relation to their metadata is enabled through integrated, flexible filtering based on expressions. Furthermore, groups of images can be compared through selection and automated aggregated metadata visualization. We showcase our method in three case studies involving the domains of photography, machine learning, and medical imaging.
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    Where did my Lines go? Visualizing Missing Data in Parallel Coordinates
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Bäuerle, Alex; Onzenoodt, Christian van; Kinderen, Simon der; Westberg, Jimmy Johansson; Jönsson, Daniel; Ropinski, Timo; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    We evaluate visualization concepts to represent missing values in parallel coordinates. We focus on the trade-off between the ability to perceive missing values and the concept's impact on common tasks. For this purpose, we identified three missing value representation concepts: removing line segments where values are missing, adding a separate, horizontal axis onto which missing values are projected, and using imputed values as a replacement for missing values. For the missing values axis and imputed values concepts, we additionally add downplay and highlight variations. We performed a crowd-sourced, quantitative user study with 732 participants comparing the concepts and their variations using five real-world datasets. Based on our findings, we provide suggestions regarding which visual encoding to employ depending on the task at focus.