Detecting image purpose in World Wide Web documents

The number of World-Wide Web (WWW) documents available to users of the Internet is growing at an incredible rate. Therefore, it is becoming increasingly important to develop systems that aid users in searching, ltering, and retrieving information from the Internet. Currently, only a few prototype systems catalog and index images in Web documents. To greatly improve the cataloging and indexing of images on the Web, we have developed a prototype rule-based system that detects the content images in Web documents. Content images are images that are associated with the main content of Web documents, as opposed to a multitude of other images that exist in Web documents for di erent purposes, such as decorative, advertisement and logo images. We present a system that uses decision tree learning for automated rule induction for the content image detection system. The system uses visual features, text-related features and the document context of images in concert for fast and e ective content image detection in Web documents. We have evaluated the system by collecting more than 1200 images from 4 di erent Web sites and we have achieved an overall classi cation accuracy of 84%.