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CVPR
2009
IEEE
16 years 11 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...
147
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CVPR
2009
IEEE
16 years 11 months ago
Co-training with Noisy Perceptual Observations
Many perception and multimedia indexing problems involve datasets that are naturally comprised of multiple streams or modalities for which supervised training data is only sparsely...
Ashish Kapoor, Chris Mario Christoudias, Raquel Ur...
ICCV
2009
IEEE
16 years 9 months ago
Saliency Driven Total Variation Segmentation
This paper introduces an unsupervised color segmentation method. The underlying idea is to segment the input image several times, each time focussing on a different salient part...
Michael Donoser, Martin Urschler, Martin Hirzer an...
ICCV
2009
IEEE
1821views Computer Vision» more  ICCV 2009»
16 years 9 months ago
Feature Correspondence and Deformable Object Matching via Agglomerative Correspondence Clustering
We present an efficient method for feature correspondence and object-based image matching, which exploits both photometric similarity and pairwise geometric consistency from local ...
Minsu Cho (Seoul National University), Jungmin Lee...
CVPR
1999
IEEE
16 years 6 months ago
Fundamental Bounds on Edge Detection: An Information Theoretic Evaluation of Different Edge Cues
We treat the problem of edge detection as one of statistical inference. Local edge cues, implemented by filters, provide information about the likely positions of edges which can ...
Scott Konishi, Alan L. Yuille, James M. Coughlan, ...
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