2021/12/11 by Mohammad Bajammal, Ali Mesbah, Bajammal, Mohammad +1 · 1 citation
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Video Analysis and Summarization #Web Applications and Data Management #Web Data Mining and Analysis #cs.CV
paper · pdf · doi:10.48550/arxiv.2112.11975
arxiv created 2021/12/11 · openalex publication_date 2021/12/11 · arxiv updated 2021/12/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Page segmentation is a web page analysis process that divides a page into cohesive segments, such as sidebars, headers, and footers. Current page segmentation approaches use either the DOM, textual content, or rendering style information of the page. However, these approaches have a number of drawbacks, such as a large number of parameters and rigid assumptions about the page, which negatively impact their segmentation accuracy. We propose a novel page segmentation approach based on visual analysis of localized adjacency regions. It combines DOM attributes and visual analysis to build features of a given page and guide an unsupervised clustering. We evaluate our approach on 35 real-world web pages, and examine the effectiveness and efficiency of segmentation. The results show that, compared with state-of-the-art, our approach achieves an average of 156% increase in precision and 249% improvement in F-measure.