  Person Re-IDentification (Re-ID) aims to match person images captured from
two non-overlapping cameras. In this paper, a deep hybrid similarity learning
(DHSL) method for person Re-ID based on a convolution neural network (CNN) is
proposed. In our approach, a CNN learning feature pair for the input image pair
is simultaneously extracted. Then, both the element-wise absolute difference
and multiplication of the CNN learning feature pair are calculated. Finally, a
hybrid similarity function is designed to measure the similarity between the
feature pair, which is realized by learning a group of weight coefficients to
project the element-wise absolute difference and multiplication into a
similarity score. Consequently, the proposed DHSL method is able to reasonably
assign parameters of feature learning and metric learning in a CNN so that the
performance of person Re-ID is improved. Experiments on three challenging
person Re-ID databases, QMUL GRID, VIPeR and CUHK03, illustrate that the
proposed DHSL method is superior to multiple state-of-the-art person Re-ID
methods.
