  Person re-identification has received special attention by the human analysis
community in the last few years. To address the challenges in this field, many
researchers have proposed different strategies, which basically exploit either
cross-view invariant features or cross-view robust metrics. In this work, we
propose to exploit a post-ranking approach and combine different feature
representations through ranking aggregation. Spatial information, which
potentially benefits the person matching, is represented using a 2D body model,
from which color and texture information are extracted and combined. We also
consider background/foreground information, automatically extracted via Deep
Decompositional Network, and the usage of Convolutional Neural Network (CNN)
features. To describe the matching between images we use the polynomial feature
map, also taking into account local and global information. The Discriminant
Context Information Analysis based post-ranking approach is used to improve
initial ranking lists. Finally, the Stuart ranking aggregation method is
employed to combine complementary ranking lists obtained from different feature
representations. Experimental results demonstrated that we improve the
state-of-the-art on VIPeR and PRID450s datasets, achieving 67.21% and 75.64% on
top-1 rank recognition rate, respectively, as well as obtaining competitive
results on CUHK01 dataset.
