Combining classifiers to identify online databases
Proceedings of the 16th international conference on World Wide Web
A Hierarchy of Support Vector Machines for Pattern Detection
The Journal of Machine Learning Research
On the Design of Cascades of Boosted Ensembles for Face Detection
International Journal of Computer Vision
A coarse-to-fine taxonomy of constellations for fast multi-class object detection
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Using the idea of the sparse representation to perform coarse-to-fine face recognition
Information Sciences: an International Journal
Branch&Rank for Efficient Object Detection
International Journal of Computer Vision
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Coarse-to-fine classification is an efficient way of organizing object recognition in order to accommodate a large number of possible hypotheses and to systematically exploit shared attributes and the hierarchical nature of the visual world. The basic structure is a nested representation of the space of hypotheses and a corresponding hierarchy of (binary) classifiers. In existing work, the representation is manually crafted. Here we introduce a design principle for recursively learning the representation and the classifiers together. This also unifies previous work on cascades and tree-structured search. The criterion for deciding when a group of hypotheses should be "retested" (a cascade) versus partitioned into smaller groups ("divide-and-conquer") is motivated by recent theoretical work on optimal search strategies. The key concept is the cost-to-power ratio of a classifier. The learned hierarchy consists of both linear cascades and branching segments and outperforms manual ones in experiments on face detection.