Webpage Segmentation for Extracting Images and Their Surrounding Contextual Information
This addresses the issue of mining contextual information for web images, which is useful for tasks like image annotation and clustering, but the approach appears incremental.
The paper tackles the problem of extracting web images and their surrounding contextual information by proposing a webpage segmentation algorithm, and experiments show it achieves better results than an existing method.
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.