  Web images come in hand with valuable contextual information. Although this
information has long been mined for various uses such as image annotation,
clustering of images, inference of image semantic content, etc., insufficient
attention has been given to address issues in mining this contextual
information. In this paper, we propose a webpage segmentation algorithm
targeting the extraction of web images and their contextual information based
on their characteristics as they appear on webpages. We conducted a user study
to obtain a human-labeled dataset to validate the effectiveness of our method
and experiments demonstrated that our method can achieve better results
compared to an existing segmentation algorithm.
