用深度特征相似度评估可视化效果，比传统方法更贴近人眼判断。
Seeing Eye to AI? Applying Deep-Feature-Based Similarity Metrics to Information Visualization
- 采用五种模型和三组预训练权重，扩展深度特征相似度度量
- 在散点图与视觉通道相似性上复现了众包研究结果
- 使用ImageNet预训练权重的指标优于优化过的MS-SSIM
评估可视化之间的相似性对可视化搜索、推荐系统等应用至关重要。近期研究表明，基于深度特征的相似度度量与图像感知相似性高度相关，且在图像超分辨率、风格迁移等任务中表现优异。本文探索此类度量在可视化相似性判断中的应用。我们扩展了一种相似度度量，采用五种机器学习架构和三组预训练权重。在散点图与视觉通道相似性感知方面，复现了先前众包研究的结果。值得注意的是，使用ImageNet预训练权重的度量在性能上优于经梯度下降调优的多尺度结构相似性（MS-SSIM）——该指标基于亮度、对比度和结构。本工作有助于理解深度特征度量如何提升可视化相似性评估，可能改进可视化分析工具与技术。补充材料可在 https://osf.io/dj2ms 获取。
原文摘要 · Abstract (English)
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.
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