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'''image representations including higher-order features and attributes''' '''object detection and localization''' '''image segmentation''' '''large margin classifiers including multiple kernel learning''' '''conditional random fields and topic models''' '''learning (with) taxonomies''' '''active learning and user feedback''' '''other topics''' |
Block-Seminar "Machine Learning for Computer Vision"
Language
English
Termine und Informationen
Erster Termin für Themenvergabe |
Mittwoch, 21.10.2009, 11-13 Uhr, Raum FR 6046 |
Weitere Termine und Raum |
Nach Absprache |
verantwortlich |
Prof. Dr. Klaus-Robert Mueller |
Ansprechtpartner(in) |
Paul von Buenau |
Sprechzeiten |
nach Vereinbarung |
Themen
Das Blockseminar wird Artikel der Machine Learning Methoden fuer die Aufgaben in Computer Vision besprechen.
- image representations including higher-order features and attributes
- object and scene categorization
- object detection and localization
- image segmentation
- large margin classifiers including multiple kernel learning
- conditional random fields and topic models
- learning (with) taxonomies
- active learning and user feedback
Literatur und Links
object and scene categorization
G. Csurka, C. Bray, C. Dance, and L. Fan, “Visual categorization with bags of keypoints,”in Workshop on Statistical Learning in Computer Vision, ECCV, Prague, Czech Republic, May 2004, pp. 1–22.
http://www.cs.utexas.edu/~grauman/courses/spring2007/395T/papers/csurka_dance_bags_keypoints.pdf
Betreuer:
S. Lazebnik, C. Schmid, and J. Ponce, “Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, New York, USA, June 2006, pp. 2169–2178.
Betreuer:
M. Varma and D. Ray, “Learning the discriminative power-invariance trade-off,” in Proceedings of the IEEE 11th International Conference on Computer Vision (ICCV ’07), 2007, pp. 1–8.
http://dx.doi.org/10.1109/ICCV.2007.4408875
Betreuer:
image representations including higher-order features and attributes
object detection and localization
image segmentation
large margin classifiers including multiple kernel learning
conditional random fields and topic models
learning (with) taxonomies
active learning and user feedback
other topics