Efficient Region-Based Image Retrieval
||Efficient Region-Based Image Retrieval
||R. Weber, M.Mlivoncic
||12th International Conference on Information and Knowledge Management (CIKM'03)New Orleans, LA, USA
||Institute of Information Systems, ETH Zurich
Region-based image retrieval (RBIR) was recently proposed as an
extension of content-based image retrieval (CBIR). An RBIR system
automatically segments images into a variable number of regions, and
extracts for each region a set of features. Then, a dissimilarity function
determines the distance between a database image and a set of reference
regions. Unfortunately, the large evaluation costs of the dissimilarity
function are restricting RBIR to relatively small databases. In this
paper, we apply a multi-step approach to enable region-based techniques
for large image collections. We provide cheap lower and upper bounding
distance functions for a recently proposed dissimilarity measure. As
our experiments show, these bounding functions are so tight, that we
have to evaluate the expensive distance function for less than 0.5% of
the images. For a typical image database with more than 370,000 images,
our multi-step approach improved retrieval performance by a factor of
more than 5 compared to the currently fastest methods.
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