Interactive Hypergraph Visual Analytics for Exploring Large and Complex Image Collections

Analyzing large complex image collections in domains like forensics, accident investigation, or social media analysis involves interpreting intricate, overlapping relationships among images. Traditional clustering and classification methods fail to adequately represent these complex relationships, particularly when labeled data or suitable pre-trained models are unavailable. Hypergraphs effectively capture overlapping relationships, but to translate their complexity into information and insights for domain expert users visualization is essential. We propose an interactive visual analytics approach specifically designed for the construction, exploration, and analysis of hypergraphs on large-scale complex image collections. Our core contributions include: (1) a scalable pipeline for constructing hypergraphs directly from raw image data, including a similarity measure to evaluate constructed hypergraphs against a ground truth, (2) interactive visualization techniques that integrate spatial hypergraph representations, interactive grids, and matrix visualizations, enabling users to dynamically explore and interpret relationships without becoming overwhelmed and disoriented, and (3) practical insights on how domain experts can effectively use the application, based on evaluation with real-life image collections. Our results demonstrate that our visual analytics approach facilitates iterative exploration, enabling domain experts to efficiently derive insights from image collections containing tens of thousands of images.