Seeing Eye to AI? Applying Deep-Feature-Based Similarity Metrics to Information Visualization
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866929739560648704 |
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| author | Long, Sheng Chatzimparmpas, Angelos Alexander, Emma Kay, Matthew Hullman, Jessica |
| author_facet | Long, Sheng Chatzimparmpas, Angelos Alexander, Emma Kay, Matthew Hullman, Jessica |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_00228 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Seeing Eye to AI? Applying Deep-Feature-Based Similarity Metrics to Information Visualization Long, Sheng Chatzimparmpas, Angelos Alexander, Emma Kay, Matthew Hullman, Jessica Human-Computer Interaction Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Seeing Eye to AI? Applying Deep-Feature-Based Similarity Metrics to Information Visualization |
| topic | Human-Computer Interaction Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2503.00228 |