Classifying images on the web automatically

Numerous research works about the extraction of low-level features from images and videos have been published. However, only recently the focus has shifted to exploiting low-level features to classify images and videos automatically into semantically broad and meaningful categories. In this paper, novel classification algorithms are presented for three broad and general-purpose categories. In detail, we present algorithms for distinguishing photo-like images from graphical images, actual photos from only photo-like, but artificial images and presentation slides/scientific posters from comics. On a large image database, our classification algorithm achieved an accuracy of 97.69% in separating photo-like images from graphical images. In the subset of photo-like images, true photos could be separated from ray-traced/rendered image with an accuracy of 97.3%, while with an accuracy of 99.5% the subset of graphical images was successfully partitioned into presentation slides/scientific posters and comics.

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