1. Scalable Person Re-identification on Supervised Smoothed Manifold
Song Bai, Xiang Bai, Qi Tian
Huazhong University of Science and Technology
University of Texas at San Antonio

2. Task Definition
Search a probe in a gallery with the help of the training set
Training set Y in pairwise constraints
Testing set = Probe p + Gallery X
Cam-A
Probe
Cam-B
Gallery
Remark: Pairwise-constrainted labels are different from category labels.
2018/4/12
Supervised Smoothed Manifold

3. Affinity learning in Person Re-identification
Most Previous works
(Feature + Metric Learning)
Our contribution
(Supervised Smoothed Manifold)
Remark: a new direction which can be investigated.
2018/4/12
Supervised Smoothed Manifold

4. Motivation The illustration of the feature space True matching pairs
A positive pair (labeled)
An unlabeled pair
A negative pair (labeled)
True matching pairs
False matching pairs
2018/4/12
Supervised Smoothed Manifold

5. Supervised Smoothed Manifold
Solution
On-line
Construct the affinity graph with the gallery X and training set Y;
Do affinity learning by propagating the pairwise-constrained labels;
Return the manifold parameterized by the learning affinity.
Off-line
Input a given probe image;
Do probe embedding on the manifold;
Return the matching probabilities.
More details can be found in the paper.
2018/4/12
Supervised Smoothed Manifold

6. Supervised Smoothed Manifold
Advantage
Three merits:
Supervision: take advantage of the supervision in pairwise constraints, which is easily accessible in this task.
Efficiency: highly-efficient on-line indexing.
Generalization: a post-processing procedure (or a generic tool) to further boost the identification accuracies of most existing algorithms.
2018/4/12
Supervised Smoothed Manifold

7. Experimental Comparison
Datasets: GRID, VIPeR, PRID450S, CUHK03 and Market-1501.
Feature: LOMO, GOG, EFL6 and ResNet.
Metric: Euclidean and XQDA.
Boost the performances of various features and metrics.
2018/4/12
Supervised Smoothed Manifold

8. Experimental Comparison
A new state-of-the-art on five benchmark datasets.
Datasets
R=1
R=5
R=10
R=20
mAP
GRID
24.80 -
58.40
68.88
27.20
61.12
70.56
VIPeR
63.92
91.49
96.65
53.73
96.08
PRID450S
68.40
94.50
97.80
72.98
96.76
99.11
CUHK03 (labeled)
75.3
91.0
96.0
76.6
94.6
98.0
CUHK03 (detected)
65.5
88.4
94.8
72.7
92.4
96.1
Market-1501
76.04
48.45
88.18
76.18
Remark: for each dataset, the 1st row presents the best performances to date, and the 2nd row presents our results.
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Supervised Smoothed Manifold

9. Supervised Smoothed Manifold
Conclusion
Addresses person re-identification using affinity learning (or re-ranking, post-ranking), which did not receive enough attention in this field.
A simple yet effective tool for various existing algorithms to further improve their performances.
2018/4/12
Supervised Smoothed Manifold

10. Thanks！
Codes are available at