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IJCV
2011
264views more  IJCV 2011»
13 years 1 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
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
ICCV
2009
IEEE
1824views Computer Vision» more  ICCV 2009»
14 years 11 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
ICMCS
2007
IEEE
155views Multimedia» more  ICMCS 2007»
14 years 14 days ago
Hidden Maximum Entropy Approach for Visual Concept Modeling
Recently, the bag-of-words approach has been successfully applied to automatic image annotation, object recognition, etc. The method needs to first quantize an image using the vis...
Sheng Gao, Joo-Hwee Lim, Qibin Sun
PAMI
2012
11 years 8 months ago
CPMC: Automatic Object Segmentation Using Constrained Parametric Min-Cuts
—We present a novel framework to generate and rank plausible hypotheses for the spatial extent of objects in images using bottom-up computational processes and mid-level selectio...
João Carreira, Cristian Sminchisescu