Web image context extraction based on semantic representation of web page visual segments

Abstract

Among the most challenging scientific interests of the past years, special attention has been given to the task of web image information mining. Web images exist in huge amounts on the web and several methods for their efficient description and representation have been proposed so far. In many of the exploited algorithms, web image information is extracted from textual sources such as image file names, anchor texts, existing keywords and, of course, surrounding text. However, the systems that attempt to mine information for images using surrounding text suffer from several problems, such as the inability to correctly assign all relevant text to an image and discard the irrelevant text at the same time. A novel method for indexing web images is discussed in the present paper. The proposed system uses visual cues in order to obtain a web page segmentation. The segments are represented with semantic metrics and a k-means clustering assigns these segments to the web image they refer to. The evaluation procedure indicates that the semantic representation method of the visual segments delivers a good description for the web images.