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Learning Taxonomies in Large Image Databases Lokesh Setia and Hans Burkhardt

Summary: Learning Taxonomies in Large Image Databases
Lokesh Setia and Hans Burkhardt
Chair for Pattern Recognition and Image Processing
Albert-Ludwigs-University Freiburg
79110 Freiburg im Breisgau, Germany
{setia, burkhardt} @informatik.uni-freiburg.de
Growing image collections have created a need for effec-
tive retrieval mechanisms. Although content-based image
retrieval systems have made huge strides in the last decade,
they often are not sufficient by themselves. Many databases,
such as those at Flickr are augmented by keywords supplied
by its users. A big stumbling block however lies in the fact
that many keywords are actually similar or occur in common
combinations which is not captured by the linear metadata
system employed in the databases. This paper proposes a
novel algorithm to learn a visual taxonomy for an image
database, given only a set of labels and a set of extracted
feature vectors for each image. The taxonomy tree could
be used to enhance the user search experience in several


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


Collections: Computer Technologies and Information Sciences