Page Segmentation using Visual Adjacency Analysis
This work addresses page segmentation for web analysis, offering significant improvements in accuracy, though it is incremental as it builds on existing methods.
The paper tackles the problem of web page segmentation by proposing a novel approach based on visual adjacency analysis, which combines DOM attributes and visual analysis for unsupervised clustering, achieving an average 156% increase in precision and 249% improvement in F-measure compared to state-of-the-art methods on 35 real-world web pages.
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.