Mining Visual Knowledge on the World Wide Web for Generic Image Classification

Abstract

In this paper, we describe a generic image classification system with an automatic knowledge acquisition mechanism from the World Wide Web. Due to the recent spread of digital imaging devices, the demand for generic image classification/recognition of various kinds of real world scenes becomes greater. To realize it, visual knowledge on various kinds of scenes is required, and we have to prepare a large number of learning images. Therefore, commercial image collections such as Corel Image Gallery are widely used in conventional studies on generic image classification. However, they are not suitable as learning images for generic image classification, since they do not include various kinds of images. Then, in stead of commercial image collections we propose utilizing of a large number of images gathered from the World-Wide Web by a Web image-gathering system as learning images. Images on the Web have huge diversity in general. So that we take advantage of the diversity of Web images for a generic image classification task, which is the first attempt among this kinds of work. By the experiments, we show that utilizing of Web images as learning images is effective and promising for generic image classification.