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» Image Segmentation as Learning on Hypergraphs
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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...
CIVR
2003
Springer
126views Image Analysis» more  CIVR 2003»
15 years 2 months ago
Learning in Region-Based Image Retrieval
In this paper, several effective learning algorithms using global image representations are adjusted and introduced to region-based image retrieval (RBIR). First, the query point m...
Feng Jing, Mingjing Li, Lei Zhang, HongJiang Zhang...
ACCV
2010
Springer
14 years 4 months ago
Learning Image Structures for Optimizing Disparity Estimation
We present a method for optimizing the stereo matching process when it is applied to a series of images with similar depth structures. We observe that there are similar regions wit...
M. V. Rohith, Chandra Kambhamettu
75
Voted
ISBI
2004
IEEE
15 years 10 months ago
Enhanced Snake-Based Segmentation of Vocal Folds
We present a system to segment the medial edges of the vocal folds from stroboscopic video. The system has two components. The first learns a color transformation that optimally d...
Sonya Allin, John M. Galeotti, George D. Stetten, ...
IJCV
2011
264views more  IJCV 2011»
14 years 4 months ago
Cost-Sensitive Active Visual Category Learning
Abstract We present an active learning framework that predicts the tradeoff between the effort and information gain associated with a candidate image annotation, thereby ranking un...
Sudheendra Vijayanarasimhan, Kristen Grauman