A new classification model using privileged information and its application

In human's behavior and cognition, teachers always play an important role. However, in the field of machine learning, the information offered by the teacher is seldom applied. In this paper, inspired by Vapnik et al., we propose a fast learning model using privileged information, which uses two smaller-sized Linear Programming (LP) model to take place of a larger Quadratic Programming (QP) model and applies two nonparallel hyperplanes to construct the final classifier. After that, we introduce the Learning model Using Privileged Information (LUPI) into the Visual Tracking Object (VOT) field, which can accelerate the convergence rate of learning and effectively improve the quality. In detail, we give the clear definition of the privileged information about VOT problem and propose a simple but effective on-line object tracking algorithm using privileged information, and all experimental results show the robustness and effectiveness of the proposed method, at the same time show the privileged information provides a great help for further improving the quality.