Automatic Shoeprint Retrieval Algorithm for
Real Crime Scenes Supplemental Material
Xinnian Wang, Huihui Sun, Qing Yu, and Chi Zhang
Dalian Maritime University, Dalian, China
In the supplementary material, we give more details about the parameters
selection (Section 1) and the performance of anti-geometry distortion (Section
2), and provide more examples of the retrieval results (Section 3).
1
Parameters Selection
There are four main parameters of the proposed algorithm, which are Cth (sT ),
Cth (sB ), rTop and rBottom . This experiment is to select the best value for every
parameter, and the experiment is conducted on the gallery set GS1 . The selection
process includes two steps. The first step is to estimate the confidence value
threshold, and the second step is to estimate the default similarity measure of
regions with lower confidence value. At the first step, Cth (sT ) is set to be a
real value between 0.01 and 0.1. Cth (sB ) is set to be equal to Cth (sT ). rTop
is estimated by the similarity measure of the bottom region, and rBottom is
estimated by the similarity measure of the top region. The test results are shown
in Tab. 1. It can be found that the cumulative match score of top 2% is the
highest one when the confidence value threshold is set to 0.03, and we choose
0.03 as the confidence value threshold. At the second step, the confidence value
is fixed, and the estimated similarity is set a real value between 0.1 and 0.5. The
results are shown in Tab. 2. We have found that the cumulative match score can
reach the highest when the estimated similarity is 0.3.
Table 1. Matching accuracy of different confidence value thresholds
Confidence Value Top
0.1%
0.01
29.4%
0.03
29.2%
0.05
27.8%
2
Ranks of Retrieval Results
Top Top Top Top Top
0.2% 0.3% 0.4% 0.5% 1%
46.6% 57.0% 63.7% 66.9% 81.2%
45.6% 55.8% 62.5% 66.3% 80.2%
44.1% 54.2% 60.5% 64.3% 78.0%
Top
2%
87.3%
88.1%
86.1%
Performance of Anti-geometry Distortion
To verify the anti-geometry distortion ability of the proposed algorithm, 72 test
images are respectively inputted into the retrieval algorithm, and statistically
2
Xinnian Wang, Huihui Sun, Qing Yu, and Chi Zhang
Table 2. Matching accuracy of different estimated similarity values
Similarity Values Top
0.1%
0.18
47.8%
0.3
45.2%
0.5
31.0%
Ranks of Retrieval Results
Top
Top
Top
Top
Top
0.2% 0.3% 0.4% 0.5%
1%
65.1% 71.6% 74.4% 76.8% 80.8%
64.1% 69.4% 73.8% 75.8% 81.8%
44.4% 49.0% 54.6% 57.7% 64.9%
Top
2%
85.1%
87.5%
71.6%
count the number of their synthetically generated geometry distortion versions
lying in the top 7 in the ranked list of results. Since the gallery includes the
test images, the first one in the sorted list is the test image itself. In theory, the
transformed versions of the test image should be at the top of the sorted list,
but in practice, the generated images may lose some information because of out
of range, and some images of the same category may also lie in the top 7. Tab. 3
shows in order indexes of six geometry distortion versions of one image fron each
group in the ranked lists. Tab. 4 shows the ratios of geometry distortion versions
lying in the top 7 in the ranked lists. Although the average percent is just more
than 81%, the left 19% images are almost from the same class. These results
show that the proposed algorithm is robust to geometry distortions.
Table 3. Percentage of geometry distortion versions lying in the top 7 in the ranked
lists
Group number Percentage of geometry distortion versions lying in the top 7 matches
1
2
3
4
5
6
7
8
9
10
11
12
3
85.7%
94.05%
71.4%
95.24%
85.71%
76.19%
76.19%
71.43%
78.57%
75%
91.84%
71.43%
Examples of The Retrieval Results
The top 20 images of the retrieval results of one test image randomly chosen
from each group are shown in Fig.1 - Fig.12, and the tests are conducted on
Automatic Shoeprint Retrieval Algorithm for Real Crime Scenes
3
Table 4. Order indexes of different geometry distortion versions lying in the top 7 in
the ranked lists
Geometry Distortion
G1
versions
I1
upward translated
5
versions
downward translated
2
versions
left translated
3
versions
right translated
4
versions
clockwise rotation
8
versions
anticlockwise rotation
7
versions
Test Image
G2 G3 G4 G5 G6 G7 G8 G9 G10 G11 G12
I2 I3 I4 I5 I6 I7 I8 I9 I10 I11 I12
5
2
2
5
2
4
2
2
5
3
3
4
5
4
4
4
2
4
3
4
2
2
2
1
5
3
5
3
3
5
3
4
5
3
3
3
2
3
5
5
4
2
5
4
6 15 6
9 10 6 11 8
6
7
18
7 18 7
8 13 7 27 7
35
6
27
the database composed of 210,000 shoeprints. The top one of the ranked list is
the test image itself. It can be found that the top 20 images in the ranked lists
almost have the same patterns as the test images and the orders of the ranked
images are similar to human’s subjective evaluation results.
4
Xinnian Wang, Huihui Sun, Qing Yu, and Chi Zhang
Fig. 1. Retrieval result of one image in Group 1
Fig. 2. Retrieval result of one image in Group 2
Automatic Shoeprint Retrieval Algorithm for Real Crime Scenes
Fig. 3. Retrieval result of one image in Group 3
Fig. 4. Retrieval result of one image in Group 4
5
6
Xinnian Wang, Huihui Sun, Qing Yu, and Chi Zhang
Fig. 5. Retrieval result of one image in Group 5
Fig. 6. Retrieval result of one image in Group 6
Automatic Shoeprint Retrieval Algorithm for Real Crime Scenes
Fig. 7. Retrieval result of one image in Group 7
Fig. 8. Retrieval result of one image in Group 8
7
8
Xinnian Wang, Huihui Sun, Qing Yu, and Chi Zhang
Fig. 9. Retrieval result of one image in Group 9
Fig. 10. Retrieval result of one image in Group 10
Automatic Shoeprint Retrieval Algorithm for Real Crime Scenes
Fig. 11. Retrieval result of one image in Group 11
Fig. 12. Retrieval result of one image in Group 12
9