System for Screening Objectionable Images
Using Daubechies' Wavelets and Color Histograms

James Ze Wang, Gio Wiederhold, Oscar Firschein
Stanford University, Stanford, CA 94305

This paper describes WIPE (Wavelet Image Pornography Elimination), an algorithm capable of classifying an image as objectionable or benign. The algorithm uses a combination of Daubechies' wavelets, normalized central moments, and color histograms to provide semantically-meaningful feature vector matching so that comparisons between the query image and images in a pre-marked training set can be performed efficiently and effectively. The system is practical for real-world applications, processing queries at the speed of less than 10 seconds each, including the time to compute the feature vector for the query. Besides its exceptional speed, it has demonstrated 97.5% recall over a test set of 437 images found from objectionable news groups. It wrongly classified 18.4% of a set of 10,809 benign images obtained from various sources. For different application needs, the algorithm can be adjusted to show 95.2% recall while wrongly classifying only 10.7% of the benign images.

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Citation: James Z. Wang, Gio Wiederhold and Oscar Firschein, ``System for Screening Objectionable Images Using Daubechies' Wavelets and Color Histograms,'' Lecture Notes in Computer Science, Special issue on interactive distributed multimedia systems and telecommunication services, Darmstadt, Germany, Ralf Steinmetz and Lars C. Wolf (eds.), vol. 1309, pp. 20-30, Springer-Verlag, September 1997.

Copyright 1997 Springer-Verlag. Published in the Proceedings of IDMS'97, September 10-12, 1997 in Germany. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the Springer-Verlag.

Last Modified: 01-Mar-97 22:10:45 PST
1997, James Z. Wang