HPC-Vis: A Visual Analytic System for Interactive Exploration of Historical Painter Cohorts

TL;DR

HPC-Vis is a visual analytic system that enables interactive exploration of Chinese historical painter cohorts by reconstructing inheritance relationships, classifying artistic styles, and providing a collaborative exploration mechanism.

Contribution

The paper introduces a novel three-stage algorithm for reconstructing painter inheritance graphs and a unified style labeling system using large language models, enhancing cohort analysis.

Findings

Effective visualization of complex inheritance networks as mountain maps.

Successful application in case studies with positive user feedback.

Improved accuracy in cohort identification compared to traditional methods.

Abstract

More than ten thousand Chinese historical painters are recorded in the literature; their cohort analysis has always been a key area of research on Chinese painting history for both professional historians and amateur enthusiasts. However, these painters have very diverse artistic styles and an extremely complex network of inheritance relationships (e.g., master-apprentice or style imitation relationships); traditional cohort analysis methods not only heavily rely on field experience, but also cost a lot of time and effort with numerous but scattered historical documents. In this paper, we propose HPC-Vis, a visual analytical system for interactive exploration of historical painter cohorts. Firstly, a three-stage reconstruction algorithm for inheritance relationships of painters is proposed, which automatically converts the complex relationship graph of historical painters into a forest structure that contains multiple trees with clear inheriting chains, and we visually encoded this forest as a mountain map to intuitively show potential cohorts of historical painters. Secondly, a unified artistic style label system with three levels (i.e., subjects, techniques, and emotions) is established by using large language models, and it is further visually encoded as a new foldable nested doughnut chart. Finally, a visually guided human-computer collaborative interactive exploration mechanism is constructed, in which a painter cohort recommendation model is designed by integrating style, identity, time, space, and relationships. Two case studies and a user study demonstrate the advantage of HPC-Vis on assisting historians in discovering, defining, and validating cohorts of historical painters.