Generic image classification using visual knowledge on the web

  • Authors:

  • Keiji Yanai

  • Affiliations:

  • The University of Electro-Communications, Chofugaoka, Chofu-shi, Tokyo, JAPAN

  • Venue:
  • MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
  • Year:
  • 2003

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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 image recognition of various kinds of real world scenes becomes greater. For realizing it, visual knowledge on various kinds of scenes is required. Then, we propose gathering visual knowledge on real world scenes for generic image classification from the World-Wide Web. Our system gathers a large number of images from the Web automatically and makes use of them as training images for generic image classification. It consists of three modules, which are an image-gathering module, an image-learning module and an image classification module. The image-gathering module gathers images related to given class keywords from the Web automatically. The learning module extracts image features from gathered images and associates them with each class. The image classification module classifies an unknown image into one of the classes corresponding to the class keywords by using the association between image features and classes. In the experiments, we achieved a classification rate 44.6% for generic images by using images gathered from the World-Wide Web automatically as training images.