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CVPR
2009
IEEE
15 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...
ICPR
2002
IEEE
14 years 10 months ago
Relational Graph Labelling Using Learning Techniques and Markov Random Fields
This paper introduces an approach for handling complex labelling problems driven by local constraints. The purpose is illustrated by two applications: detection of the road networ...
Denis Rivière, Jean-Francois Mangin, Jean-M...
EMMCVPR
1997
Springer
14 years 1 months ago
Genetic Algorithms for Ambiguous Labelling Problems
Consistent labelling problems frequently have more than one solution. Most work in the "eld has aimed at disambiguating early in the interpretation process, using only local ...
Richard Myers, Edwin R. Hancock
CVPR
2011
IEEE
13 years 5 months ago
Learning A Discriminative Dictionary for Sparse Coding via Label Consistent K-SVD
A label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse coding is presented. In addition to using class labels of training data, we also assoc...
Zhuolin Jiang, Zhe Lin, Larry Davis
ICCV
2009
IEEE
15 years 29 days ago
Sparsity Induced Similarity Measure for Label Propagation
Graph-based semi-supervised learning has gained considerable interests in the past several years thanks to its effectiveness in combining labeled and unlabeled data through labe...
Hong Cheng, Zicheng Liu, Jie Yang