Identification of Leading Lines in Artworks Based on Topological Analysis of Saliency Maps

Fuminori Shibasaki, Issei Fujishiro

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A key compositional technique, leading lines guide the observer's gaze across visual scenes. This study presents a novel approach to the visual analysis of leading lines in artworks by applying discrete Morse theory to saliency maps refined through persistent homology. The saliency map's gradient field is abstracted into a structured representation using the maximum graph, enabling the identification of both explicit and implicit leading lines. To enhance the analysis, texture gradient cues are incorporated to embed depth information into the saliency map. Our visual analysis system also features semiautomatic thresholding and tools to depict the direction of visual guidance. An experimental study with participants from an art university validated the system's feasibility by analyzing various Western paintings. This research contributes to the field of digital and public humanities and computational art analysis by offering an environment in which to explore visual composition.

Original languageEnglish
Title of host publicationProceedings - 2025 IEEE 18th Pacific Visualization Conference, PacificVis 2025
PublisherIEEE Computer Society
Pages369-374
Number of pages6
ISBN (Electronic)9798331505813
DOIs
Publication statusPublished - 2025
Event18th IEEE Pacific Visualization Conference, PacificVis 2025 - Tokyo, Japan
Duration: 2025 Apr 222025 Apr 25

Publication series

NameIEEE Pacific Visualization Symposium
ISSN (Print)2165-8765
ISSN (Electronic)2165-8773

Conference

Conference18th IEEE Pacific Visualization Conference, PacificVis 2025
Country/TerritoryJapan
CityTokyo
Period25/4/2225/4/25

Keywords

  • digital humanities
  • leading lines
  • topological data analysis
  • Visualization

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Software

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