Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Clustering Websites by Salient Design Features. |
| Συγγραφείς: |
Kaluarachchi, Thisaranie, Dissanayake, Sumedhe, Wickramasinghe, Manjusri |
| Πηγή: |
Journal of Image & Graphics (United Kingdom); 2026, Vol. 14 Issue 2, p230-258, 29p |
| Θεματικοί όροι: |
Self-organizing maps, Image processing, Design templates, Automatic classification, Web design, Clustering algorithms, Automation software |
| Περίληψη: |
Designing websites to meet user and client expectations often requires repeated refinement cycles, making the process time-consuming and resource-intensive. This study proposes an automated classification system that categorizes real-world websites based on their salient structural design features to support data-driven automatic website generation. The system integrates Self-Organizing Maps (SOMs) with a novel image-processing pipeline that combines edge detection, gradient analysis, and morphological filtering and image smoothing to extract structural wireframe layouts from website screen captures. Experiments were conducted on three datasets: manually created wireframes, screen captures of top 100 websites, and screen captures of top 1500 websites ranked by SimilarWeb. The analysis revealed seven representative layout archetypes: dashboard interfaces, simple information pages, fixed-width product grids, informational pages with sidebars, basic search interfaces, multi-section content layouts, and tabular data interfaces. The classification quality was evaluated using topographic error, quantization error, Silhouette coefficient, and Davies-Bouldin index, demonstrating consistent and meaningful clustering. Our findings highlight the potential of SOM-based clustering for automatic website template generation, offering a scalable and data-driven foundation for design automation and frontend prototyping. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: |
Complementary Index |