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CVPR
2009
IEEE
16 years 4 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...
CVPR
2008
IEEE
15 years 11 months ago
Latent topic random fields: Learning using a taxonomy of labels
An important problem in image labeling concerns learning with images labeled at varying levels of specificity. We propose an approach that can incorporate images with labels drawn...
Xuming He, Richard S. Zemel
CVPR
2000
IEEE
15 years 11 months ago
Towards Automatic Discovery of Object Categories
We propose a method to learn heterogeneous models of object classes for visual recognition. The training images contain a preponderance of clutter and learning is unsupervised. Ou...
Markus Weber, Max Welling, Pietro Perona
TMI
2011
182views more  TMI 2011»
14 years 4 months ago
Active Volume Models for Medical Image Segmentation
—In this paper, we propose a novel predictive model, active volume model (AVM), for object boundary extraction. It is a dynamic “object” model whose manifestation includes a ...
Tian Shen, Hongsheng Li, Xiaolei Huang
ICIP
2009
IEEE
15 years 10 months ago
On The Role Of Context In Probabilistic Models Of Visual Saliency
In recent years, many principled probabilistic definitions for the determination of visual saliency have been proposed. Moreover, there has been increased focus on the role of con...