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Fast and Accurate Image Matching with Cascade Hashing for 3D

Reconstruction

J Cheng CVPR14

Hyunchul Yang( 양현철 )

1. Background

2. Related work

3. Approach

4. Experiments

5. Result

6. Conclusion

Background

3D Reconstruction technology is similar with image retrieval

Background

Feature Matching is very computational cost in 3D reconstruction

Feature

Extraction

Around 50%

Running time

Feature

Matching

(Hashing)

Track

Generation

Geometric

Estimation

3D Reconstruction

Related work

KD-Tree

Well known nearest neighbor search algorithm.

But it not suitable for high dimensional space

Related work

LDAHash(Linear Discriminant Analysis) PAMI 2012

Related work

LDAHash(Linear Discriminant Analysis) PAMI 2012

Related work

LDAHash(Linear Discriminant Analysis) PAMI 2012

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.

3. Approach

Approach

• Cascade Hashing ( 3 Step )

1. Hashing Lookup with Multiple tables

2. Hashing Remapping

3. Top k Ranking via Hashing

Coarse search

Approach

• Cascade Hashing ( 3 Step )

1. Hashing Lookup with Multiple tables

2. Hashing Remapping

3. Top k Ranking via Hashing

Refined search

Approach

• Cascade Hashing ( 3 Step )

1. Hashing Lookup with Multiple tables

2. Hashing Remapping

3. Top k Ranking via Hashing

Brute search

Approach

1. Hashing Lookup with Multiple tables

1 st hash table

L = Try Count, Number of tables m = Number of hyper-planes

Approach

1. Hashing Lookup with Multiple tables

2 nd hash table

L = Try Count, Number of tables m = Number of hyper-planes

Approach

1. Hashing Lookup with Multiple tables

L th hash table

L = Try Count, Number of tables m = Number of hyper-planes

Approach

1. Hashing Lookup with Multiple tables

L = Number of tables m = Number of hyper-planes

Ex) m = 8

L = 6

Coarse search

Approach

2. Hashing Remapping

Approach

2. Hashing Remapping n = Number of hyper-planes

Approach

2. Hashing Remapping n = Number of hyper-planes

3

2

1

0

2

3

2

2

4

3

Approach

2. Hashing Remapping n = Number of hyper-planes

Ex) n = 128

Refined search

Approach

3. Top k Ranking via Hashing

Approach

3. Top k Ranking via Hashing

Brute-force search on top k bucket

4. Result

Result

Standard Oxford dataset with SIFT key points

Result

Standard Oxford dataset with SIFT key points x 10

Result

Standard Oxford dataset with SIFT key points x 15

Result

Standard Oxford dataset with SIFT key points x 100

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?

Thank you

Q & A

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