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...

• Type: Journal
• Year: 2011
• Where: ICCV
• Authors: Tianshi Gao, Daphne Koller
• Keywords: Computer Vision, Generalization Error, Hierarchy Structure, ICCV 2011, Vision Communities