  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.
