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What and Who
Title:Generalizations of the Multicut Problem for Computer Vision
Speaker:Evgeny Levinkov
coming from:Max-Planck-Institut für Informatik - D2
Speakers Bio:
Event Type:Promotionskolloquium
Visibility:D1, D3, D4, RG1, MMCI, D2, D5, SWS
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Level:Public Audience
Language:English
Date, Time and Location
Date:Monday, 8 April 2019
Time:11:30
Duration:60 Minutes
Location:Saarbrücken
Building:E1 4
Room:024
Abstract
Abstract: Graph decompositions have always been an essential part of computer vision as a large variety of tasks can be formulated in this framework. One way to optimize for a decomposition of a graph is called the multicut problem, also known as correlation clustering. Its particular feature is that the number of clusters is not required to be known a priori, but is rather deduced from the optimal solution. On the downside, it is NP-hard to solve.

In this thesis we present several generalizations of the multicut problem, that allow to model richer dependencies between nodes of a graph. We also propose efficient local search algorithms, that in practice find good solution in reasonable time, and show applications to image segmentation, multi-human pose estimation, multi-object tracking and many others.

Contact
Name(s):Connie Balzert
Phone:0681 9325 2000
Video Broadcast
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Created:
Connie Balzert/MPI-INF, 03/26/2019 12:13 PM
Last modified:
Uwe Brahm/MPII/DE, 04/08/2019 07:01 AM
  • Connie Balzert, 04/01/2019 09:40 AM
  • Connie Balzert, 03/26/2019 12:13 PM -- Created document.