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Classifying objectionable websites based on image content

  • James Ze WangEmail author
  • Jia Li
  • Gio Wiederhold
  • Oscar Firschein
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1483)

Abstract

This paper describes IBCOW (Image-based Classification of Objectionable Websites), a system capable of classifying a website as objectionable or benign based on image content. The system uses WIPE (Wavelet Image Pornography Elimination) and statistics to provide robust classification of on-line objectionable World Wide Web sites. Semantically-meaningful feature vector matching is carried out so that comparisons between a given on-line image and images marked as ”objectionable” and ”benign” in a training set can be performed efficiently and effectively in the WIPE module. If more than a certain number of images sampled from a site is found to be objectionable, then the site is considered to be objectionable. The statistical analysis for determining the size of the image sample and the threshold number of objectionable images is given in this paper. The system is practical for real-world applications, classifying a Web site at a speed of less than 2 minutes each, including the time to compute the feature vector for the images downloaded from the site, on a Pentium Pro PC. Besides its exceptional speed, it has demonstrated 97% sensitivity and 97% specificity in classifying a Web site based solely on images. Both the sensitivity and the specificity in real-world applications is expected to be higher because our performance evaluation is relatively conservative and surrounding text can be used to assist the classification process.

Keywords

Wipe System Objectionable Image Image Query Correct Classification Rate Wipe Module 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • James Ze Wang
    • 1
    Email author
  • Jia Li
    • 1
  • Gio Wiederhold
    • 1
  • Oscar Firschein
    • 1
  1. 1.Stanford UniversityStanfordUSA

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