Matt, MatthiasZeppelzauer, MatthiasWaldner, ManuelaEl-Assady, MennatallahSchulz, Hans-Jörg2024-05-212024-05-212024978-3-03868-253-0https://doi.org/10.2312/eurova.20241113https://diglib.eg.org/handle/10.2312/eurova20241113We present cVIL, a class-centric approach to visual interactive labeling, which facilitates human annotation of large and complex image data sets. cVIL uses different property measures to support instance labeling for labeling difficult instances and batch labeling to quickly label easy instances. Simulated experiments reveal that cVIL with batch labeling can outperform traditional labeling approaches based on active learning. In a user study, cVIL led to better accuracy and higher user preference compared to a traditional instance-based visual interactive labeling approach based on 2D scatterplots.Attribution 4.0 International LicenseCCS Concepts: Human-centered computing → Visual analytics; User interface designHuman centered computing → Visual analyticsUser interface design10.2312/eurova.202411136 pages