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Feature Selection for Automatic Image Lokesh Setia and Hans Burkhardt

Summary: Feature Selection for Automatic Image
Lokesh Setia and Hans Burkhardt
Albert-Ludwigs-University Freiburg
79110 Freiburg im Breisgau, Germany
{setia, burkhardt}@informatik.uni-freiburg.de
Abstract. Automatic image annotation empowers the user to search an
image database using keywords, which is often a more practical option
than a query-by-example approach. In this work, we present a novel
image annotation scheme which is fast and effective and scales well to a
large number of keywords. We first provide a feature weighting scheme
suitable for image annotation, and then an annotation model based on
the one-class support vector machine. We show that the system works
well even with a small number of visual features. We perform experiments
using the Corel Image Collection and compare the results with a well-
established image annotation system.
1 Introduction
The amount of available multimedia data is continuously on the rise. With this
arises the need to be able to locate existing data effectively. Data which cannot
easily be found is as good as lost. Multimedia search differs from text search


Source: Albert-Ludwigs-Universität Freiburg, Institut für Informatik,, Lehrstuhls für Mustererkennung und Bildverarbeitung


Collections: Computer Technologies and Information Sciences