Academic Journal
Clustering Websites by Salient Design Features.
| Title: | Clustering Websites by Salient Design Features. |
|---|---|
| Authors: | Kaluarachchi, Thisaranie, Dissanayake, Sumedhe, Wickramasinghe, Manjusri |
| Source: | Journal of Image & Graphics (United Kingdom); 2026, Vol. 14 Issue 2, p230-258, 29p |
| Subject Terms: | Self-organizing maps, Image processing, Design templates, Automatic classification, Web design, Clustering algorithms, Automation software |
| Abstract: | 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] |
| Copyright of Journal of Image & Graphics (United Kingdom) is the property of Journal of Image & Graphics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Clustering Websites by Salient Design Features. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kaluarachchi%2C+Thisaranie%22">Kaluarachchi, Thisaranie</searchLink><br /><searchLink fieldCode="AR" term="%22Dissanayake%2C+Sumedhe%22">Dissanayake, Sumedhe</searchLink><br /><searchLink fieldCode="AR" term="%22Wickramasinghe%2C+Manjusri%22">Wickramasinghe, Manjusri</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of Image & Graphics (United Kingdom); 2026, Vol. 14 Issue 2, p230-258, 29p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Self-organizing+maps%22">Self-organizing maps</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Design+templates%22">Design templates</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+classification%22">Automatic classification</searchLink><br /><searchLink fieldCode="DE" term="%22Web+design%22">Web design</searchLink><br /><searchLink fieldCode="DE" term="%22Clustering+algorithms%22">Clustering algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Automation+software%22">Automation software</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of Image & Graphics (United Kingdom) is the property of Journal of Image & Graphics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.18178/joig.14.2.230-258 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 29 StartPage: 230 Subjects: – SubjectFull: Self-organizing maps Type: general – SubjectFull: Image processing Type: general – SubjectFull: Design templates Type: general – SubjectFull: Automatic classification Type: general – SubjectFull: Web design Type: general – SubjectFull: Clustering algorithms Type: general – SubjectFull: Automation software Type: general Titles: – TitleFull: Clustering Websites by Salient Design Features. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kaluarachchi, Thisaranie – PersonEntity: Name: NameFull: Dissanayake, Sumedhe – PersonEntity: Name: NameFull: Wickramasinghe, Manjusri IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 23013699 Numbering: – Type: volume Value: 14 – Type: issue Value: 2 Titles: – TitleFull: Journal of Image & Graphics (United Kingdom) Type: main |
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