Feature selection for automatic image annotation

Authors:
Lokesh Setia;Hans Burkhardt
Affiliations:
Albert-Ludwigs-University Freiburg, Freiburg im Breisgau, Germany;Albert-Ludwigs-University Freiburg, Freiburg im Breisgau, Germany
Venue:
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Year:
2006

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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.