Block-Seminar "Machine Learning for Computer Vision"

Language

English

Termine und Informationen

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