Title: Automatic annotation of online articles based on visual feature classification

Authors: Radek Burget, Ivana Burgetova

Addresses: Faculty of Information Technology, Brno University of Technology, Bozetechova 2, 612 66 Brno, Czech Republic. ' Faculty of Information Technology, Brno University of Technology, Bozetechova 2, 612 66 Brno, Czech Republic

Abstract: When applying the traditional data mining methods to World Wide Web documents, the typical problem is that a normal web page contains a variety of information of different kinds in addition to its main content. This additional information such as navigation, advertisement or copyright notices negatively influences the results of the data mining methods as for example the content classification. In this paper, we present a method of interesting area detection in a web page. This method is inspired by an assumed human reader approach to this task. First, basic visual blocks are detected in the page and subsequently, the purpose of these blocks is guessed based on their visual appearance. We describe a page segmentation method used for the visual block detection, we propose a way of the block classification based on the visual features and finally, we provide an experimental evaluation of the method on real-world data.

Keywords: automatic annotation; online articles; page segmentation; document preprocessing; visual features; visual analysis; data mining; visual feature classification; web documents; online papers; web pages; visual block detection; visual blocks; document annotation.

DOI: 10.1504/IJIIDS.2011.041322

International Journal of Intelligent Information and Database Systems, 2011 Vol.5 No.4, pp.338 - 360

Received: 30 Nov 2009
Accepted: 02 Oct 2010
Published online: 21 Oct 2014 *

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