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ICCV
2011
IEEE

Discriminative Learning of Relaxed Hierarchy for Large-scale Visual Recognition

8 years 9 months ago
Discriminative Learning of Relaxed Hierarchy for Large-scale Visual Recognition
In the real visual world, the number of categories a classifier needs to discriminate is on the order of hundreds or thousands. For example, the SUN dataset [24] contains 899 scene categories and ImageNet [6] has 15,589 synsets. Designing a multiclass classifier that is both accurate and fast at test time is an extremely important problem in both machine learning and computer vision communities. To achieve a good trade-off between accuracy and speed, we adopt the relaxed hierarchy structure from [15], where a set of binary classifiers are organized in a tree or DAG (directed acyclic graph) structure. At each node, classes are colored into positive and negative groups which are separated by a binary classifier while a subset of confusing classes is ignored. We color the classes and learn the induced binary classifier simultaneously using a unified and principled max-margin optimization. We provide an analysis on generalization error to justify our design. Our method has been test...
Tianshi Gao, Daphne Koller
Added 11 Dec 2011
Updated 11 Dec 2011
Type Journal
Year 2011
Where ICCV
Authors Tianshi Gao, Daphne Koller
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