Learning The Discriminative Power-Invariance Trade-Off

1. Manik Varma Debajyoti Ray Presented by Evan and Suporn Learning The Discriminative Power-Invariance Trade-Off

2. Outline Motivation Background Learning weights Experiments Discussion

3. Motivation Image Categorization Problem Descriptors No single descriptor for all tasks Use learning 6 6 9 9 4 4

4. Background: SVM

5. SVM

6. SVM (2/|w|)‏

7. SVM

8. SVM (single kernel)‏ • Primal • Dual, linear kernel • Dual, general kernel

9. Single-kernel classifier Combination of basis kernels Learn α, d simultaneously Multiple Kernel Learning

10. Learning weights Not QP  inefficient to solve Dual now looks like ,d + σTd where

11. Efficient Multiple Kernel Learning Dual of T: Reformulate the problem as

12. Efficient Multiple Kernel Learning d0 α* α*,d* SVM dn+1 Max 1Tα + σd - ½ ΣkdkαTYKkYα gradient descent: α with 0 ≤ α ≤ C with 1tYα = 0

13. Experiment: UIUC Texture

14. Experiment: UIUC Texture

15. Experiment: Oxford Flower

16. Experiment: Oxford Flower

17. Experiment: CalTech 101

18. Experiment: CalTech 101

19. Results

20. Discussion Why do they not tackle “large-scale problems involving hundreds of kernels”? Would that help? Is the claim “what distinguishes one descriptor from another is the trade-off between discriminative power and invariance” true? Should researchers stop looking for a miracle descriptor? Potential of discriminative classification vs. other uses of distance functions over images (eg k-nn)?