Vision-based Page Segmentation Algorithm Research Articles

Application on Web Page Filtering Technology

Web page filtering technology intends to filter out the large number of the repeated and theme-unrelated noise information and obtain useful information. Some web filtering methods cannot make full use of the layout and visual features. In view of the new mainstream “DIV+CSS” designing style of modern commercial web sites, this paper summarizes that elements laying in the same div blocks have common semantic features and proposed a DIV_FOREST model to represent the web pages. And in combination with the Vision-based Page Segmentation Algorithm, a DVPS Algorithm which considers both layout features and visual features was proposed to improve web page filtering efficiency.

Design and Implementation of Hidden based Web Retrieval using Innovative Vision-based Segmentation

assimilate the extracted information from a conference website to acquire the clean and high superiority academic data. This research has subsequent contributors: We propose a novel vision-based page segmentation algorithm, which use DOM tree to compensate the information loss of classical vision-based segmentation algorithm. We transform the conference Web material extraction which is difficult into a classification problematic, and categorize text blocks as predefined sets permitting to vision, key disputes, text and content information. We improve the classification quality by post-processing. Our experimental results on real-world datasets shows that our method is highly effective and efficient for extracting academic information from conference pages.

Clustering and searching WWW images using link and page layout analysis

Due to the rapid growth of the number of digital images on the Web, there is an increasing demand for an effective and efficient method for organizing and retrieving the available images. This article describes iFind, a system for clustering and searching WWW images. By using a vision-based page segmentation algorithm, a Web page is partitioned into blocks, and the textual and link information of an image can be accurately extracted from the block containing that image. The textual information is used for image indexing. By extracting the page-to-block, block-to-image, block-to-page relationships through link structure and page layout analysis, we construct an image graph. Our method is less sensitive to noisy links than previous methods like PageRank, HITS, and PicASHOW, and hence the image graph can better reflect the semantic relationship between images. Using the notion of Markov Chain, we can compute the limiting probability distributions of the images, ImageRanks, which characterize the importance of the images. The ImageRanks are combined with the relevance scores to produce the final ranking for image search. With the graph models, we can also use techniques from spectral graph theory for image clustering and embedding, or 2-D visualization. Some experimental results on 11.6 million images downloaded from the Web are provided in the article.