1. Fast and Accurate Image Matching with Cascade Hashing for 3D Reconstruction
J Cheng et al,. CVPR14
Hyunchul Yang( 양현철 )
Jian Cheng* (Chinese Academy of Sciences), Cong Leng (Chinese Academy of Science), Jiaxiang Wu (Chinese Academy of Sciences), Hainan Cui, Hanqing Lu (NLPR , IACAS)
Hello everyone.
I’ll start my presentation. The title is fast and accurate image matching with cascade hashing for 3d reconstructions

2. Background Related work Approach Experiments Result Conclusion
Overview
Background
Related work
Approach
Experiments
Result
Conclusion

3. 3D Reconstruction technology is similar with image retrieval
Background
3D Reconstruction technology is similar with image retrieval

4. Feature Matching is very computational cost in 3D reconstruction
Background
Feature Matching is very computational cost in 3D reconstruction
Around 50%
Running time
Feature
Extraction
Feature
Matching
(Hashing)
Track Generation
Geometric
Estimation
3D Reconstruction

5. Related work KD-Tree Well known nearest neighbor search algorithm.
But it not suitable for high dimensional space

6. Related work
LDAHash(Linear Discriminant Analysis) PAMI 2012

7. Related work
LDAHash(Linear Discriminant Analysis) PAMI 2012

8. Related work
LDAHash(Linear Discriminant Analysis) PAMI 2012

9. P is projection matrix that is designed either
Related work
LDAHash(Linear Discriminant Analysis) PAMI 2012
Px + t = 0
P is projection matrix that is designed either
to solely minimize the in-class covariance of the descriptor or
to jointly minimize the in-class covariance and maximize the covariance across classes
t is threshold matrix so that the resulting binary strings maximize recognition rates.

10. 3. Approach

11. Approach Cascade Hashing ( 3 Step ) Coarse search
Hashing Lookup with Multiple tables
Hashing Remapping
Top k Ranking via Hashing
Coarse search

12. Approach Cascade Hashing ( 3 Step ) Refined search
Hashing Lookup with Multiple tables
Hashing Remapping
Top k Ranking via Hashing
Refined search

13. Approach Cascade Hashing ( 3 Step ) Brute search
Hashing Lookup with Multiple tables
Hashing Remapping
Top k Ranking via Hashing
Brute search

14. Approach Hashing Lookup with Multiple tables 1st hash table
L = Try Count, Number of tables
m = Number of hyper-planes

15. Approach Hashing Lookup with Multiple tables 2nd hash table
L = Try Count, Number of tables
m = Number of hyper-planes

16. Approach Hashing Lookup with Multiple tables Lth hash table
L = Try Count, Number of tables
m = Number of hyper-planes

17. Approach Hashing Lookup with Multiple tables Ex) m = 8 L = 6
L = Number of tables
m = Number of hyper-planes
Ex)
m = 8
L = 6
Coarse search

18. Approach
2. Hashing Remapping

19. Approach
2. Hashing Remapping
n = Number of hyper-planes

20. Approach 2. Hashing Remapping n = Number of hyper-planes 3 1 2 2 3 3 23 2 2 4

21. Approach 2. Hashing Remapping Ex) n = 128 Refined search
n = Number of hyper-planes
Ex)
n = 128
Refined search

22. Approach
3. Top k Ranking via Hashing

23. Approach 3. Top k Ranking via Hashing
Brute-force search on top k bucket

24. 4. Result

25. Result
Standard Oxford dataset with SIFT key points

26. Result
Standard Oxford dataset with SIFT key points
x 10

27. Result
Standard Oxford dataset with SIFT key points
x 15

28. Result
Standard Oxford dataset with SIFT key points
x 100

29. Conclusion
Paper proposed a Cascade Hashing method to speed up the image matching
Accelerated by our approach in hundreds times than brute force matching
Even achieves ten times or more than Kd-tree based matching
While retaining comparable accuracy.
How about apply this idea to spherical hashing?

30. Thank you Q & A