deep-feature-based similarity metric
Seeing Eye to AI? Applying Deep-Feature-Based Similarity Metrics to Information Visualization
Long, Sheng, Chatzimparmpas, Angelos, Alexander, Emma, Kay, Matthew, Hullman, Jessica
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.
- Asia > Japan > Honshū > Kantō > Kanagawa Prefecture > Yokohama (0.05)
- North America > United States > Illinois > Cook County > Evanston (0.04)
- North America > United States > New York > New York County > New York City (0.04)
- (6 more...)