J Cheng et al,. CVPR14 Hyunchul Yang( 양현철 )

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Presentation transcript:

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

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

3D Reconstruction technology is similar with image retrieval Background 3D Reconstruction technology is similar with image retrieval http://techtalks.tv/talks/fast-and-accurate-image-matching-with-cascade-hashing-for-3d-reconstruction/59776/

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 http://techtalks.tv/talks/fast-and-accurate-image-matching-with-cascade-hashing-for-3d-reconstruction/59776/ 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

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.

3. Approach

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

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

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

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

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

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

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

Approach 2. Hashing Remapping

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

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

Approach 2. Hashing Remapping Ex) n = 128 Refined search 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