Learning Visual Similarity Measures for Comparing Never Seen Objects

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 Matching Hypothesis Generation Hypothesis Verification

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 Malik Faces in the News Dataset Dataset

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

14. Results 1.) Toy Cars 2.) Ferencz 3.) Faces 4.) 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);