Michael Mlivoncic

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Complex similarity queries, i.e., multi-feature multi-object queries, are needed to express the information need of a user against a large multimedia repository. Even if a user initially issues a single-object query over one feature, a system with relevance feedback will automatically generate a complex similarity query. Relevance feedback is only useful if(More)
Nowadays, digital libraries are inherently dispersed over several peers of a steadily increasing network. Dedicated peers may provide specialized, computationally expensive services such as image similarity search. Usually, the peers of such a network are uncoordinated in the sense that their content and services are not linked together. Nevertheless, users(More)
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.(More)
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(More)
The Database Group has a long tradition in combining fundamental research and prototype development that evaluates research concepts. This is reflected by many publications and demonstrator systems that we have produced over the last thirteen years since the group's establishment at ETH Zurich in 1988. This document gives a brief overview of our current(More)
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