  For long time, person re-identification and image search are two separately
studied tasks. However, for person re-identification, the effectiveness of
local features and the "query-search" mode make it well posed for image search
techniques.
  In the light of recent advances in image search, this paper proposes to treat
person re-identification as an image search problem. Specifically, this paper
claims two major contributions. 1) By designing an unsupervised Bag-of-Words
representation, we are devoted to bridging the gap between the two tasks by
integrating techniques from image search in person re-identification. We show
that our system sets up an effective yet efficient baseline that is amenable to
further supervised/unsupervised improvements. 2) We contribute a new high
quality dataset which uses DPM detector and includes a number of distractor
images. Our dataset reaches closer to realistic settings, and new perspectives
are provided.
  Compared with approaches that rely on feature-feature match, our method is
faster by over two orders of magnitude. Moreover, on three datasets, we report
competitive results compared with the state-of-the-art methods.
