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ScrollTimes: Tracing the Provenance of Paintings as a Window into History

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arxiv 2306.08834 v2 pith:3YTRQ4DE submitted 2023-06-15 cs.HC cs.CV

classification cs.HCcs.CV
keywords handscrollprovenanceanalysishistoryculturalapproachartifactcreate
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The study of cultural artifact provenance, tracing ownership and preservation, holds significant importance in archaeology and art history. Modern technology has advanced this field, yet challenges persist, including recognizing evidence from diverse sources, integrating sociocultural context, and enhancing interactive automation for comprehensive provenance analysis. In collaboration with art historians, we examined the handscroll, a traditional Chinese painting form that provides a rich source of historical data and a unique opportunity to explore history through cultural artifacts. We present a three-tiered methodology encompassing artifact, contextual, and provenance levels, designed to create a "Biography" for handscroll. Our approach incorporates the application of image processing techniques and language models to extract, validate, and augment elements within handscroll using various cultural heritage databases. To facilitate efficient analysis of non-contiguous extracted elements, we have developed a distinctive layout. Additionally, we introduce ScrollTimes, a visual analysis system tailored to support the three-tiered analysis of handscroll, allowing art historians to interactively create biographies tailored to their interests. Validated through case studies and expert interviews, our approach offers a window into history, fostering a holistic understanding of handscroll provenance and historical significance.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. VisTCP: A Visualization Framework to Construct Knowledge-Graph-Based Representation for Traditional Chinese Painting

    cs.HC 2026-07 unverdicted novelty 5.0 of 10

    VisTCP pairs a TCP-trained structured-representation model with joint-embedding uncertainty views so historians can iteratively build knowledge-graph annotations of Traditional Chinese Paintings.

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