19 December 2001 Automatic classification of images on the Web
Author Affiliations +
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.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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

Back to Top