A boosting approach to content-based 3D model retrieval
Proceedings of the 5th international conference on Computer graphics and interactive techniques in Australia and Southeast Asia
Event recognition: viewing the world with a third eye
MM '08 Proceedings of the 16th ACM international conference on Multimedia
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AdaBoost has received considerable attention in the vision and multimedia research community in recent years. It is originally designed for two-class classification problems. To handle multiple classes, many AdaBoost extensions have been developed primarily based on various schemes for reducing multiclass classification to multiple two-class problems. From a statistical prospective, AdaBoost can be viewed as a forward stepwise additive model using an exponential loss function. In this paper, we derive a generalized form of AdaBoost for multiclass classification based on a multiclass exponential loss function. To prove its effectiveness, we benchmarked a number of multimedia problems of different nature. Experimental results show that the new boosting algorithm outperforms other multiclass alternatives. In addition, the generalized boosting algorithm can be used to either boost a multiclass classifier, or build a multiclass classifier from a binary one.