A new ensemble-based cascaded framework for multiclass training with simple weak learners
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Coarse-to-fine multiclass learning and classification for time-critical domains
Pattern Recognition Letters
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Building robust and fast object detection systems is an important goal of computer vision. A problem arises when several object types are to be detected, because the computational burden of running several specific classifiers in parallel becomes a problem. In addition the accuracy and the training time can be greatly affected. Seeking to provide a solution to these problems, we extend cascade classifiers to the multiclass case by proposing the use of multiclass coarse-to-fine (CTF) nested cascades. The presented results show that the proposed system scales well with the number of classes, both at training and running time.