Semantic Hierarchical Exploration of Large Image Datasets
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Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
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.
Description
CCS Concepts: Human-centered computing -> Graphical user interfaces; Web-based interaction; Visual analytics
@inproceedings{10.2312:evs.20231051,
booktitle = {EuroVis 2023 - Short Papers},
editor = {Hoellt, Thomas and Aigner, Wolfgang and Wang, Bei},
title = {{Semantic Hierarchical Exploration of Large Image Datasets}},
author = {Bäuerle, Alex and Onzenoodt, Christian van and Jönsson, Daniel and Ropinski, Timo},
year = {2023},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-219-6},
DOI = {10.2312/evs.20231051}
}