1. Learning Visual Similarity Measures for Comparing Never Seen Objects By: Eric Nowark, Frederic Juric Presented by: Khoa Tran

2. Outline  1.) Purpose  2.) Methodology  3.) Results

3. Purpose Object Recognition Train Images Test Images

4. Methodology Preview A.) Corresponding patch pair B.) Quantizing patch pair C.) Patch pair similarity measure

5. Object Recognition  1.) Images  2.) Feature Extraction  3.) Model Database  4.) Matching  a.) Hypothesis Generation  b.) Hypothesis Verification Images Features Extraction Model Database Hypothesis Generation Hypothesis Verification Matching

6. Images  Total: - 225 images, - 21 different objects  Training Data Set - 1185 positive image pairs - 7330 negative image pairs - 14 different objects  Testing Data Set - 1044 positive image pairs - 6337 negative image pairs - 7 different objects

7. Feature Extraction  Patches  Normalized Cross Correlation  SIFT Descriptors  Matrix representation

8. Model Database  Extremely Randomized Binary Decision Tree  SIFT Descriptors  Geometric Information  Information Gain

9. Model Database – SIFT Descriptors

10. Model Database

11. Hypothesis Generation – Similar Measure  Similar Measure  Support Vector Machine

12. Hypothesis Generation Ferencz and MalikFaces in the NewsDataset

13. C.) Hypothesis Verification  Sammon mapping for toy cars

14. Results 1.) Toy Cars2.) Ferencz 3.) Faces4.) Coil 100

15. Reference  Eric Nowak and Fredric Jurie; "Learning Visual Similarity Measures for Comparing Never Seen Objects” ;Computer Vision and Pattern Recognition 2007 (CVPR'07);