Presentation on 2nd seminar, KUET

1. Shape Recognition and Retrieval
Based on Edit Distance and
Dynamic Programming
PAN Hongfei ( 潘鸿飞 ), LIANG Dong ( 梁 栋 ), TANG Jun ( 唐
俊 ), WANG Nian ( 王 年 ), LI Wei ( 李 薇 )
Presented by
Kaidul Islam
0907016
1

2. Glimpse of this Paper
 A shape recognition and retrieval algorithm
 How the algorithm approaches:
- Extracting skeletal features(Medial axis
transform)
- Transforming features into string of symbols
- Similarity measurement(Edit Distance)
- Shape identification(Dynamic programming)
 Analyzing public datasets for comparison with
other approaches.
2

3. Structure
 Shape Representation
- Shape skeleton
- Skeleton feature representation
 Computation of Similarity between Strings
 Shape matching Dynamic Programming
 Test and Analysis
- Matching Analysis
- Comparisons
 Future Work
3

4. Shape Skeleton(1/2)
• Skeleton feature satisfy distance and
curvature criteria
– every point on the skeleton should be
equidistant from two different boundary point
– distance between the skeleton point and the
boundary point should be less than the
maximum distance between the center of
gravity of the contour and the boundary points.
4

5. Shape Skeleton(2/2)
5

6. Structure
 Shape Representation
- Shape skeleton
- Skeleton feature representation
 Computation of Similarity between Strings
 Shape matching Dynamic Programming
 Test and Analysis
- Matching Analysis
- Comparisons
 Future Work
6

7. Transformation from a skeleton to
strings
7

8. Structure
 Shape Representation
- Shape skeleton
- Skeleton feature representation
 Computation of Similarity between Strings
 Shape matching Dynamic Programming
 Test and Analysis
- Matching Analysis
- Comparisons
 Future Work
8

9. Edit Distance!
• Edit distance is a way of quantifying how
dissimilar two strings are.
• Determine minimum number of operations
to turn one string to another.
• Operation include –
- insertion
- Deletion
- Substitution
9

10. Edit Distance(2/2)
10

11. Structure
 Shape Representation
- Shape skeleton
- Skeleton feature representation
 Computation of Similarity between Strings
 Shape matching Dynamic Programming
 Test and Analysis
- Matching Analysis
- Comparisons
 Future Work
11

12. The tabulation , D(i, j)
Seq2(j)
Seq1(i)
A
0
R
T
S
1
2
3
4
0
M
1
A
2
T
3
H
4
S
5
12

13. The tabulation , D(i, j)
Seq2(j)
Seq1(i)
A
0
0
M
1
2
3
4
0
3
H
S
2
T
T
1
A
R
4
S
5
13

14. The tabulation , D(i, j)
Seq2(j)
0
M
1
2
3
4
0
1
3
H
S
2
T
T
1
A
R
0
Seq1(i)
A
4
S
5
14

15. The tabulation , D(i, j)
Seq2(j)
0
M
1
2
3
4
0
1
2
3
H
S
2
T
T
1
A
R
0
Seq1(i)
A
4
S
5
15

16. The tabulation , D(i, j)
Seq2(j)
A
R
T
S
0
1
2
3
4
0
0
1
2
3
4
M
1
1
A
2
2
T
3
3
H
4
4
Seq1(i)
S
5
5
16

17. The tabulation , D(i, j)
Seq2(j)
A
R
T
S
0
1
2
3
4
0
0
1
2
3
4
M
1
1
1
A
2
2
T
3
3
H
4
4
Seq1(i)
S
5
5
17

18. The tabulation , D(i, j)
Seq2(j)
A
R
T
S
0
1
2
3
4
0
0
1
2
3
4
M
1
1
1
2
A
2
2
T
3
3
H
4
4
Seq1(i)
S
5
5
18

19. The tabulation , D(i, j)
Seq2(j)
A
R
T
S
0
1
2
3
4
0
0
1
2
3
4
M
1
1
1
2
3
4
A
2
2
1
2
3
4
T
3
3
H
4
4
Seq1(i)
S
5
5
19

20. The tabulation , D(i, j)
Seq2(j)
A
R
T
S
0
1
2
3
4
0
0
1
2
3
4
M
1
1
1
2
3
4
A
2
2
1
2
3
4
T
3
3
2
2
2
3
H
4
4
Seq1(i)
S
5
5
20

21. The tabulation , D(i, j)
Seq2(j)
A
R
T
S
0
1
2
3
4
0
0
1
2
3
4
M
1
1
1
2
3
4
A
2
2
1
2
3
4
T
3
3
2
2
2
3
H
4
4
3
3
3
3
Seq1(i)
S
5
5
4
4
4
3
21

22. The traceback
Seq2(j)
A
R
T
S
0
1
2
3
4
0
0
1
2
3
4
M
1
1
1
2
3
4
A
2
2
1
2
3
4
T
3
3
2
2
2
3
H
4
4
3
3
3
3
Seq1(i)
S
5
5
4
4
4
3
22

23. Similarity Cost by Dynamic
Programming
23

24. Structure
 Shape Representation
- Shape skeleton
- Skeleton feature representation
 Computation of Similarity between Strings
 Shape matching Dynamic Programming
 Test and Analysis
- Matching Analysis
- Comparisons
 Future Work
24

25. Matching Analysis(1/2)
Shape sets from the MPEG-7 Shape Silhouette
Database
25

26. Matching Analysis(2/2)
Comparison of results for various values of k
26

27. Structure
 Shape Representation
- Shape skeleton
- Skeleton feature representation
 Computation of Similarity between Strings
 Shape matching Dynamic Programming
 Test and Analysis
- Matching Analysis
- Comparisons
 Future Work
27

28. Comparisons(1/3)
Sample shape sets from the
MPEG-7 Shape Silhouette
Database(1)
Sample shape sets from Plant
Species Database(2)
28

29. Comparisons(2/3)
Retrieval rates for Silhouette
Database(1)
Retrieval rates for Silhouette
Database(2)
29

30. Comparisons(3/3)
30

31. Comparisons(3/3)
31

32. Structure
 Shape Representation
- Shape skeleton
- Skeleton feature representation
 Computation of Similarity between Strings
 Shape matching Dynamic Programming
 Test and Analysis
- Matching Analysis
- Comparisons
 Future Work
32

33. Future Work
• Improve the algorithm
• Introducing other features –
- Texture
- Movement
33

34. 34