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CVPR
2009
IEEE
16 years 5 months ago
What's It Going to Cost You?: Predicting Effort vs. Informativeness for Multi-Label Image Annotations
Active learning strategies can be useful when manual labeling effort is scarce, as they select the most informative examples to be annotated first. However, for visual category ...
Sudheendra Vijayanarasimhan (University of Texas a...
ICCV
2009
IEEE
16 years 2 months ago
TagProp: Discriminative Metric Learning in Nearest Neighbor Models for Image Auto-Annotation
Image auto-annotation is an important open problem in computer vision. For this task we propose TagProp, a discriminatively trained nearest neighbor model. Tags of test images a...
Matthieu Guillaumin, Thomas Mensink, Jakob Verbeek...
ICCV
2009
IEEE
16 years 2 months ago
Moving in Stereo: Efficient Structure and Motion Using Lines
We present a fast and robust system for estimating structure and motion using a stereo pair, with straight lines as features. Our first set of contributions are efficient algorit...
Manmohan Chandraker, Jongwoo Lim, David Kriegman
CVPR
2005
IEEE
15 years 12 months ago
Parameter Estimation for MRF Stereo
This paper presents a novel approach for estimating parameters for MRF-based stereo algorithms. This approach is based on a new formulation of stereo as a maximum a posterior (MAP...
Li Zhang, Steven M. Seitz
CVPR
2006
IEEE
15 years 12 months ago
Unsupervised Learning of Categories from Sets of Partially Matching Image Features
We present a method to automatically learn object categories from unlabeled images. Each image is represented by an unordered set of local features, and all sets are embedded into...
Kristen Grauman, Trevor Darrell