19 December 2001 Automatic classification of images on the Web
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Abstract
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 meaningful and broad 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, true photos from only photo-like, but artificial images and presentation slides from comics. On a large image database, our classification algorithm achieved an accuracy of 97.3% 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 87.3%, while with an accuracy of 93.2% the subset of graphical images was successfully partitioned into presentation slides and comics.
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Alexander Hartmann, Alexander Hartmann, Rainer W. Lienhart, Rainer W. Lienhart, } "Automatic classification of images on the Web", Proc. SPIE 4676, Storage and Retrieval for Media Databases 2002, (19 December 2001); doi: 10.1117/12.451108; https://doi.org/10.1117/12.451108
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