Title: Large Margin Learning in Set to Set Similarity Comparison for Person Re-identification,
Abstract: Person re-identification (Re-ID) aims at matching images of the same person
across disjoint camera views, which is a challenging problem in multimedia
analysis, multimedia editing and content-based media retrieval communities. The
major challenge lies in how to preserve similarity of the same person across
video footages with large appearance variations, while discriminating different
individuals. To address this problem, conventional methods usually consider the
pairwise similarity between persons by only measuring the point to point (P2P)
distance. In this paper, we propose to use deep learning technique to model a
novel set to set (S2S) distance, in which the underline objective focuses on
preserving the compactness of intra-class samples for each camera view, while
maximizing the margin between the intra-class set and inter-class set. The S2S
distance metric is consisted of three terms, namely the class-identity term,
the relative distance term and the regularization term. The class-identity term
keeps the intra-class samples within each camera view gathering together, the
relative distance term maximizes the distance between the intra-class class set
and inter-class set across different camera views, and the regularization term
smoothness the parameters of deep convolutional neural network (CNN). As a
result, the final learned deep model can effectively find out the matched
target to the probe object among various candidates in the video gallery by
learning discriminative and stable feature representations. Using the CUHK01,
CUHK03, PRID2011 and Market1501 benchmark datasets, we extensively conducted
comparative evaluations to demonstrate the advantages of our method over the
state-of-the-art approaches.