AG 1, AG 2, AG 3, AG 4, AG 5, SWS, RG1, MMCI   Info

Public Audience

English

60 Minutes

Saarbrücken

A large part of image analysis is about breaking things into pieces. Decompositions of a graph are a mathematical abstraction of the possible outcomes. Breaking things (like an image itself) into pieces in a controlled way is hard. Optimization problems whose feasible solutions define decompositions of a graph are a mathematical abstraction of this task. One example is the correlation clustering problem whose feasible solutions relate one-to-one to the decompositions of a graph, and whose objective function puts a cost or reward on neighboring nodes ending up in distinct components. This talk shows work by the Combinatorial Image Analysis research group that applies this problem and proposed generalizations to diverse image analysis tasks. It sketches the algorithms introduced by the group for finding feasible solutions for large instances in practice, solutions that are often superior in the metrics of application-specific benchmarks. Finally, it sketches the algorithm introduced by the group for finding lower bounds and points to new findings and open problems of polyhedral geometry in this context.

Jennifer Müller

2900

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