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CVPR
2009
IEEE
16 years 10 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...
121
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CG
2008
Springer
15 years 3 months ago
Efficient image reconstruction for point-based and line-based rendering
We address the problem of an efficient image-space reconstruction of adaptively sampled scenes in the context of point-based and line-based graphics. The imagespace reconstruction...
Ricardo Marroquim, Martin Kraus, Paulo Roma Cavalc...
135
Voted
PR
2006
117views more  PR 2006»
15 years 3 months ago
On transforming statistical models for non-frontal face verification
: We address the pose mismatch problem which can occur in face verification systems that have only a single (frontal) face image available for training. In the framework of a Bayes...
Conrad Sanderson, Samy Bengio, Yongsheng Gao
CVPR
2009
IEEE
1599views Computer Vision» more  CVPR 2009»
16 years 10 months ago
Multi-Label Sparse Coding for Automatic Image Annotation
In this paper, we present a multi-label sparse coding framework for feature extraction and classification within the context of automatic image annotation. First, each image is ...
Changhu Wang (University of Science and Technology...
136
Voted
ICIP
1998
IEEE
16 years 5 months ago
Adaptive Wavelet Packet Image Coding using an Estimation-Quantization Framework
In this paper, we extend the statistical model-based Estimation-Quantization (EQ) wavelet image coding algorithm introduced in [?] to include an adaptive transform component. For ...
Kannan Ramchandran, Mehmet Kivanç Mih&ccedi...