This work was based on an information-retrieval system that finds in a database all the images “close” to one selected image. From the selected image the software looks at thousands of images stored in the database and retrieves all the ones that are deemed “close” to the selected image. The images were tested against a set of 10,000 photographic images and a knowledge base. The knowledge base was built with a training system. For every image there is some trusted element, a feature vector can be defined that encompasses all the information, texture, color, and so on. Then images are classified according to the information in this vector.

The database contains thousands of objectionable images of various types and thousands of benign images5 of various types. In the training process, you process random images to see if the detection and classification are correct. You can adjust sensitivity parameters to allow tighter or looser filtering. You could combine text and images or do multiple processing of multiple images on one site to decrease the overall error in classifying a site as objectionable or not.

A statistical analysis was done showing that, if you download 20-35 images for each site, and 20-25 percent of downloaded images are objectionable, then you can classify the Web site as objectionable with 97 percent accuracy.6 Image content analysis can be combined with text and IP address filtering. To avoid false positives, especially for art images, you can skip images that are associated with the IP addresses of museums, dog shows, beach towns, sports events, and so on.

In summary, you cannot expect perfect filtering. There is always a trade-off between performance and processing effort. But the performance of the WIPE system shows that good results can be obtained with current technology. The performance can improve by combining imagebased and text-based processing. James Wang is working on training the system automatically as it extracts the features and then classifying the images manually as either objectionable and not.7

5  

To develop a set of benign images, David Forsyth suggested obtaining the Corel collection or some similar set of images known to be not-problematic or visiting Web news groups, where it is virtually guaranteed that images will not be objectionable. He said this is a rare case in which you can take a technical position without much trouble.

6  

David Forsyth took issue with the statistical analysis, because there is a conditional probability assumption that the error is independent of the numbers. In the example given earlier with images of puddings (in Forsyth’s talk in Chapter 3), a large improvement in performance cannot be expected because there are certain categories in which the system will just get it wrong again. If it is wrong about one picture of pudding and then wrong again about a second picture of pudding, then it will classify the Web site wrong, also.

7  

For more information, see <http://WWW-DB.Stanford.EDU/IMAGE> (papers) and <http://wang.ist.psu.edu>.



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