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Proceedings Paper

Web tools to support image classification
Author(s): F. Odone; A. Barla; E. Franceschi; A. Verri
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Paper Abstract

Content-based image classification is a wide research field addressing the problem of categorizing images according to their content. A common way to approach content-based classification is through learning from examples --- a given class of images is described by means of a suitable training set of data. The main drawback of this approach is the fact that collecting data to build homogeneous training and validation sets is a boring and time consuming task, even if the Web can help providing a potentially inexhaustible source of images. In this paper we present a system to automatically download images from the Web and a selection of techniques useful to prune the images downloaded according to some criteria. These techniques work as filters at various degrees of complexity: some are simple measurements other are image classifiers themselves. We focus on two critical ones (monochrome vs color images and photos vs graphics) showing their effectiveness on a manually labeled validation set of data. We conclude the paper analyzing the overall performance of the system with an a posteriori analysis of the results obtained in a few run.

Paper Details

Date Published: 17 January 2005
PDF: 10 pages
Proc. SPIE 5670, Internet Imaging VI, (17 January 2005); doi: 10.1117/12.586685
Show Author Affiliations
F. Odone, INFM, Univ. degli Studi di Genova (Italy)
A. Barla, INFM, Univ. degli Studi di Genova (Italy)
E. Franceschi, INFM, Univ. degli Studi di Genova (Italy)
A. Verri, INFM, Univ. degli Studi di Genova (Italy)

Published in SPIE Proceedings Vol. 5670:
Internet Imaging VI
Simone Santini; Raimondo Schettini; Theo Gevers, Editor(s)

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