# Seeing Eye to AI? Applying Deep-Feature-Based Similarity Metrics to Information Visualization Source: [Seeing Eye to AI? Applying Deep-Feature-Based Similarity Metrics to Information Visualization]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:48:55.522Z Authors: Sheng Long, Angelos Chatzimparmpas, Emma Alexander, Matthew Kay, Jessica Hullman Published: 2025-02-28T22:25:04.000Z ## Abstract Judging the similarity of visualizations is crucial to various applications, such as visualization-based search and visualization recommendation systems. Recent studies show deep-feature-based similarity metrics correlate well with perceptual judgments of image similarity and serve as effective loss functions for tasks like image super-resolution and style transfer. We explore the application of such metrics to judgments of visualization similarity. We extend a similarity metric using five ML architectures and three pre-trained weight sets. We replicate results from previous crowd-sourced studies on scatterplot and visual channel similarity perception. Notably, our metric using pre-trained ImageNet weights outperformed gradient-descent tuned MS-SSIM, a multi-scale similarity metric based on luminance, contrast, and structure. Our work contributes to understanding how deep-feature-based metrics can enhance similarity assessments in visualization, potentially improving visual analysis tools and techniques. Supplementary materials are available at https://osf.io/dj2ms. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()