Multiclass object learning with JointBoosting-GA

Most methods for multiclass objects learning have large computational complexity and samples scale complexity. In this paper, within the framework of boosting, we propose a novel method called JointBoosting-GA. It is suitable to all datasets from small to very large, and results in a much faster classifier at run time. To achieve it, we combine two ideas: 1) Firstly, we introduce a novel technique, which is based on genetic algorithm, to generate new samples. At each boosting round, it generates new samples and expands the training set. By this way, our method can avoid overfitting, and produce classifiers with high predictive accuracy. 2) Secondly, by sharing features across classes, we reduce the computational cost of the learned classifiers at run time, when detecting multiclass objects in cluttered scenes. Experiments on Caltech 101 dataset showed that, our method outperformed SVM and JointBoosting when only small samples were available for multiclass objects learning.