Person reidentification refers to the task of recognizing the same person
under different non-overlapping camera views. Presently, person
reidentification based on metric learning is proved to be effective among
various techniques, which exploits the labeled data to learn
a subspace that maximizes the inter-person divergence while minimizes
the intra-person divergence. However, these methods fail to
take the different impacts of various instances and local features into
account. To address this issue, we propose to learn a projection matrix
such that the importance of different instances and local features
are re-weighted jointly. We also come up with a simplified formulation
of the proposed algorithm, thus it can be solved by the efficient
UDFS optimization algorithm. Extensive experiments on the VIPeR
and iLIDS datasets demonstrate the effectiveness and efficiency of
our algorithm.