  In this work, we propose an end-to-end constrained clustering scheme to
tackle the person re-identification (re-id) problem. Deep neural networks (DNN)
have recently proven to be effective on person re-identification task. In
particular, rather than leveraging solely a probe-gallery similarity, diffusing
the similarities among the gallery images in an end-to-end manner has proven to
be effective in yielding a robust probe-gallery affinity. However, existing
methods do not apply probe image as a constraint, and are prone to noise
propagation during the similarity diffusion process. To overcome this, we
propose an intriguing scheme which treats person-image retrieval problem as a
{\em constrained clustering optimization} problem, called deep constrained
dominant sets (DCDS). Given a probe and gallery images, we re-formulate person
re-id problem as finding a constrained cluster, where the probe image is taken
as a constraint (seed) and each cluster corresponds to a set of images
corresponding to the same person. By optimizing the constrained clustering in
an end-to-end manner, we naturally leverage the contextual knowledge of a set
of images corresponding to the given person-images. We further enhance the
performance by integrating an auxiliary net alongside DCDS, which employs a
multi-scale Resnet. To validate the effectiveness of our method we present
experiments on several benchmark datasets and show that the proposed method can
outperform state-of-the-art methods.
