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

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.

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Publication Details

Published
2025-04-22
DOI
https://doi.org/10.1109/pacificvis64226.2025.00045
Primary Topic
Aesthetic Perception and Analysis
Type
article
Field-Weighted Citation Impact
0.00
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article

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

Issei Fujishiro, Fuminori Shibasaki
Aesthetic Perception and Analysis
article

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

Issei Fujishiro, Fuminori Shibasaki
article en

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.

Keio University (JP)
Openalex Percentile: Top 87%
Aesthetic Perception and Analysis
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