Using Daubechies ' Wavelets and Color Histograms ?

This paper describes WIPETM (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 e ciently and e ectively. The system is practical for realworld 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 classi ed 18.4% of a set of 10,809 benign images obtained from various sources. For di erent application needs, the algorithm can be adjusted to show 95.2% recall while wrongly classifying only 10.7% of the benign images.