Virtual sample generation method using modified Gaussian model and salient region

Object detection usually needs large sample set for training. We proposed a virtual sample feature generating method for small sample set. Firstly, a generating model for the subcomponent of sample feature is built by using Gaussian distribution simulation. Secondly, the relationship between subcomponents is taken into consideration and modification of generating model is introduced, which can improve the accuracy of generation. Finally, the salient region information is fused into generation model to expand the universality for different object. According to experiments on multi-database, our method effectively improved the detecting rate with small training set.