Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Adaptive Feature Embedding with Local Sample Distributions for
  Person Re-identification   Person re-identification (re-id) aims to match pedestrians observed by
disjoint camera views. It attracts increasing attention in computer vision due
to its importance to surveillance system. To combat the major challenge of
cross-view visual variations, deep embedding approaches are proposed by
learning a compact feature space from images such that the Euclidean distances
correspond to their cross-view similarity metric. However, the global Euclidean
distance cannot faithfully characterize the ideal similarity in a complex
visual feature space because features of pedestrian images exhibit unknown
distributions due to large variations in poses, illumination and occlusion.
Moreover, intra-personal training samples within a local range are robust to
guide deep embedding against uncontrolled variations, which however, cannot be
captured by a global Euclidean distance. In this paper, we study the problem of
person re-id by proposing a novel sampling to mine suitable \textit{positives}
(i.e. intra-class) within a local range to improve the deep embedding in the
context of large intra-class variations. Our method is capable of learning a
deep similarity metric adaptive to local sample structure by minimizing each
sample's local distances while propagating through the relationship between
samples to attain the whole intra-class minimization. To this end, a novel
objective function is proposed to jointly optimize similarity metric learning,
local positive mining and robust deep embedding. This yields local
discriminations by selecting local-ranged positive samples, and the learned
features are robust to dramatic intra-class variations. Experiments on
benchmarks show state-of-the-art results achieved by our method.

22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio