journal article·2025-07-15Open access

From Viewing to Structure: A Computational Framework for Modeling and Visualizing Visual Exploration

Kuan-Chen Chen, Chang-Franw Lee, Teng-Wen Chang, Cheng-Gang Wang, Jia-Rong Li

Applied Sciences· Volume 15· Issue 14

Abstract

This study proposes a computational framework that transforms eye-tracking analysis from statistical description to cognitive structure modeling, aiming to reveal the organizational features embedded in the viewing process. Using the designers’ observation of a traditional Chinese landscape painting as an example, the study draws on the goal-oriented nature of design thinking to suggest that such visual exploration may exhibit latent structural tendencies, reflected in patterns of fixation and transition. Rather than focusing on traditional fixation hotspots, our four-dimensional framework (Region, Relation, Weight, Time) treats viewing behavior as structured cognitive networks. To operationalize this framework, we developed a data-driven computational approach that integrates fixation coordinate transformation, K-means clustering, extremum point detection, and linear interpolation. These techniques identify regions of concentrated visual attention and define their spatial boundaries, allowing for the modeling of inter-regional relationships and cognitive organization among visual areas. An adaptive buffer zone method is further employed to quantify the strength of connections between regions and to delineate potential visual nodes and transition pathways. Three design-trained participants were invited to observe the same painting while performing a think-aloud task, with one participant selected for the detailed demonstration of the analytical process. The framework’s applicability across different viewers was validated through consistent structural patterns observed across all three participants, while simultaneously revealing individual differences in their visual exploration strategies. These findings demonstrate that the proposed framework provides a replicable and generalizable method for systematically analyzing viewing behavior across individuals, enabling rapid identification of both common patterns and individual differences in visual exploration. This approach opens new possibilities for discovering structural organization within visual exploration data and analyzing goal-directed viewing behaviors. Although this study focuses on method demonstration, it proposes a preliminary hypothesis that designers’ gaze structures are significantly more clustered and hierarchically organized than those of novices, providing a foundation for future confirmatory testing.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Visual Attention and Saliency Detection · Visual perception and processing mechanisms · Gaze Tracking and Assistive Technology

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Doctoral Program, National Yunlin University of Science and Technology, Yunlin, Douliou 640301, Taiwan

Department of Digital Media Design, National Yunlin University of Science and Technology, Yunlin, Douliou 640301, Taiwan

Department of Information Management, National Yunlin University of Science and Technology, Yunlin, Douliou 640301, Taiwan

Funding

National Science and Technology Council, Taiwan

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