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dc.contributor.authorTolias, Giorgos
dc.date.accessioned2022-02-24T08:45:25Z
dc.date.available2022-02-24T08:45:25Z
dc.date.issued2022
dc.identifier.urihttp://hdl.handle.net/10467/99934
dc.description.abstractThis thesis summarizes the author’s post-PhD work that has a major focus on instance-level recogni- tion tasks, such as instance-level search primarily, but also instance-level recognition. Visual representa- tion is at the core of most computer vision tasks. This is addressed in the first part of the manuscript with crafting and learning of visual representations and similarity measures. A number of different represen- tation approaches are summarized under the same framework provided by a match kernel formulation, while the interplay between local and global representation is highlighted. Given the representation, efficient estimation of similarity with respect to a large number of examples enables visual search appli- cations. This is covered in the second part especially in the form of query expansion, which goes beyond pairwise similarity and treats a collection of examples as a whole. The last part discusses object discovery as an outcome of exploiting visual similarity and search within a large unordered collection of examples. Its result is then used to improve other components, namely representation and search, therefore, forming a circle and pronouncing the synergy between different contributions in this thesis.cze
dc.language.isoenen
dc.publisherCTU in Pragueen
dc.titleFrom Visual Representation to Object Discovery and Backen
dc.typehabilitační prácecze
dc.typehabilitation thesis
theses.degree.grantorČeské vysoké učení technické v Praze. Fakulta elektrotechnická.


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