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IJCV
2011
264views more  IJCV 2011»
13 years 21 days 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
ICCV
2009
IEEE
1824views Computer Vision» more  ICCV 2009»
14 years 10 months ago
Beyond the Euclidean distance: Creating effective visual codebooks using the histogram intersection kernel
Common visual codebook generation methods used in a Bag of Visual words model, e.g. k-means or Gaussian Mixture Model, use the Euclidean distance to cluster features into visual...
Jianxin Wu, James M. Rehg
DAGM
2009
Springer
13 years 3 months ago
HMM-Based Defect Localization in Wire Ropes - A New Approach to Unusual Subsequence Recognition
Abstract. Automatic visual inspection has become an important application of pattern recognition, as it supports the human in this demanding and often dangerous work. Nevertheless,...
Esther-Sabrina Platzer, Josef Nägele, Karl-He...
CVPR
2011
IEEE
13 years 1 months ago
What You Saw is Not What You Get: Domain Adaptation Using Asymmetric Kernel Transforms
In real-world applications, “what you saw” during training is often not “what you get” during deployment: the distribution and even the type and dimensionality of features...
Brian Kulis, Kate Saenko, Trevor Darrell
NIPS
2008
13 years 7 months ago
Multi-Level Active Prediction of Useful Image Annotations for Recognition
We introduce a framework for actively learning visual categories from a mixture of weakly and strongly labeled image examples. We propose to allow the categorylearner to strategic...
Sudheendra Vijayanarasimhan, Kristen Grauman