Proposal of a classifier of images collected in the World Wide Web

This work presents a proposal of a system that classifies images collected in the World Wide Web. The system separates the images in two semantic classes: photographs and graphics. Photographs are images that show natural scenes, such as people, faces, flowers, animals, landscapes, and cities. Graphics are logos, drawings, icons, maps, and backgrounds, frequently generated by computer. To do this classification we used metrics based on difference that exist between the two images types. These metrics return a numerical value that drive to one of the two classes. To realize the classification we used a supervised technique based on the knowledge that generates rules. This technique is the ID3 method that induces a decision tree and allows to classify the images