Michael Mlivoncic

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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 ,(More)
Complex similarity queries, i.e., multi-feature multi-object queries, are needed to express the information need of a user against a large multi-media 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(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)
There exists a number of image similarity search systems that do retrieval on the whole image content. In this thesis an efficient and flexible implementation of a content based image retrieval system that works on fractions of an image, the so called regions, is discussed. The thesis presents two correct and fast algorithms that find similar images to(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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