Inverted Dirichlet and Related Distributions Based Feature Mapping for Data Classification

In this paper, we propose a distribution based feature mapping technique to improve the baseline accuracy of SVM kernels in different computer vision tasks. The proposed technique is based on learning parameters from the data and use that parameters to make inference from new data. The learned parameters can be thought of as prior knowledge about the data representation. Utilizing such prior knowledge about the data distribution increases the discriminative power of the classifier. Our proposed feature mapping technique is based on inverted Dirichlet, generalized inverted Dirichlet and inverted Beta Liouville distributions. These distributions are efficient in modelling semi-bounded data which are prevalent in computer vision problems. Our experimental results demonstrate the effectiveness of the proposed method in texture recognition, natural scene recognition and human action recognition in videos.