Image Classification for Mobile Web Browsing

• Takuya Maekawa, Grad. Sch. of Information Science and Technology Osaka University, Japan
• Takahiro Hara, Grad. Sch. of Information Science and Technology Osaka University, Japan
• Shojiro Nishio, Grad. Sch. of Information Science and Technology Osaka University, Japan

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Track: Pervasive Web and Mobility

Slot: 11:00-12:30, Wednesday 24th May

It is difficult for users of mobile devices such as cellular phones equipped with a small screen and a poor input interface to browse Web pages designed for desktop PCs with large displays. Many studies and commercial products have tried to solve this problem. Web pages include images that have various roles such as site menus, line headers for itemization, and page titles. However, most studies of mobile Web browsing haven't paid much attention to the roles of Web images. In this paper, we define eleven Web image categories according to their roles and use these categories for proper Web image handling. We manually categorized 3,901 Web images collected from forty Web sites and extracted image features of each category according to the classification. By making use of the extracted features, we devised an automatic Web image classification method. Furthermore, we evaluated the automatic classification of real Web pages and achieved up to 83.1% classification accuracy. We also implemented an automatic Web page scrolling system as an application of our automatic image classification method.