A filter and refinement approach to RBIR
||A filter and refinement approach to RBIR
||M. Mlivoncic, R. Weber
||DELOS Workshop on Multimedia Contents in Digital LibrariesChania, Crete, Greece
||Institute of Information Systems, ETH Zurich
A promising trend in content based image retrieval (CBIR) is
the incorporation of the notion of objects into the similarity
evaluation. Images are automatically segmented into a dynamic number
of regions that roughly correspond to objects. In region based image
retrieval (RBIR), we compute standard feature characteristic atop
those segments and evaluate the similarity between images based on the
similarity of its segments. This requires the application of matching
techniques, like the Hungarian algorithm, that are prohibitivly expensive
for collections beyond a few thousand images. In this paper, we provide a
filter and refinement search algorithm to solve RBIR queries in reasonable
times for even large image collections. We describe the bounding functions
as well as implementational issues of our approach. Experimental results
show that retrieval performance surpasses alternative approaches by a
factor of 5.
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