  The two-stage strategy has been widely used in image classification. However,
these methods barely take the classification criteria of the first stage into
consideration in the second prediction stage. In this paper, we propose a novel
two-stage representation method (TSR), and convert it to a Single-Teacher
Single-Student (STSS) problem in our two-stage image classification framework.
We seek the nearest neighbours of the test sample to choose candidate target
classes. Meanwhile, the first stage classifier is formulated as the teacher,
which holds the classification scores. The samples of the candidate classes are
utilized to learn a student classifier based on L2-minimization in the second
stage. The student will be supervised by the teacher classifier, which approves
the student only if it obtains a higher score. In actuality, the proposed
framework generates a stronger classifier by staging two weaker classifiers in
a novel way. The experiments conducted on several face and object databases
show that our proposed framework is effective and outperforms multiple popular
classification methods.
