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Visual Representations for Machine Learning
Course goals
This course focuses on two specific topics in representation for machine learning in computer vision applications: Spectral clustering and Channel representations. Both of these representations will be described both from a theoretical point of view and applied to various problems.
Prerequisites
Course participants are expected to have a good understanding of matrix decompositon (linear algebra, numerical methods).
Course organization
Preliminary, the course will consist of
- 4 lectures, where theory and applications are presented. The first two on spectral clustering (Klas Nordberg) and the last two on channel representations (Michael Felsberg)
- 2 seminars, course participants are expected to present excerpts from papers that describe applications and extensions of the lecture material.
The course gives 3hp to students who have attended all lectures and seminars, and have presented some amount of related material at both of the seminars.
Preliminary schedule
The course will be given during the
end of the spring term 2015, preliminary no later than June 17.
The schedule below might be subject to updates.
- Lecture 1: Spectral clustering: introduction and confusion. May, 26, Thursday, 15.15 - 1700, Algoritmen! SLIDES
- Lecture 2: Spectral clustering: from confusion to clarity. May, 30, Monday, 15.15 - 17.00. SLIDES
- Lecture 3: Introduction to channel representations. June, 2, Thursday, 15.15 - 17.00. SLIDES
- Lecture 4: Introduction to channel representations. June, 9, Thursday, 15.15 - 17.00. SLIDES
- Seminar 1: spectral clustering. June, 15, Wednesday, 15.15 - 17.00. Gustav (Govindu), Abdo (Fowlkes et al.), Andreas (Zografos et al.)
- Seminar 2: channel representations. June, 17, Friday, 10.15 - 12.00. Bertil (Suggested paper 1), Felix (Suggested paper 2), Hannes (Suggested paper 3).
Links and references
- Ulrike von Luxburg, A tutorial on spectral clustering, Stat Comput (2007) 17:395-416
- Ulrike von Luxburg, A tutorial on spectral clustering, Technical Report No TR-149, Max-Planck Institute for Bilogical Cybernetics, 2007 (A longer version of the journal paper)
- Shi & Malik, Normalized cuts and image segmentation, PAMI 22(8), 888-905, 2000
- Ng, Jordan & Weiss, On spectral clustering: analysis and an algorithm, in "Advances in Neural Information Processing Systems" 14, 849-856, 2002
- Kumar & Daumé, A Co-training Approach for Multi-view Spectral Clustering, ICML, 2011
- Govindu, A Tensor Decomposition for Geometric Grouping and Segmentation, CVPR 2005
- Chen & Lerman, Spectral Curvature Clustering, International Journal of Computer Vision, 81:317-330, 2009
- Fowlkes, Belongie, Chung & Malik, Spectral Grouping Using the Nyström Method, IEEE Transaction on Pattern Analysis and Machine Intelligence, 26(2), 2004
- Zografos, Lenz, Ringaby, Felsberg and Nordberg, Fast Segmentation of Sparse 3D Point Trajectories Using Group Theoretical Invariants, ACCV 2014
Contact information
- Klas Nordberg, klas.nordberg@liu.se
- Michael Felsberg, michael.felsberg@liu.se
Senast uppdaterad: 2016-06-09