1. D ATA D RIVEN A TTRIBUTES FOR A CTION R ECOGNITION Week 7 Presented by Christina Peterson

2. V ALIDATION /T ESTING S ETS Validation Set: Videos 1 – 4 Test Set: Video 5 Exemplar Set: Videos 6 – 10 Issues on Validation Set The exemplar-SVMs were predicting a negative label for many of the positive samples in the validation set Solutions Regularization Parameters recommended from [1] C 1 =0.5 and C 2 =0.01

3. P RECISION AND R ECALL tp: true positive fp: false positive fn: false negative

4. A CTION 1: D IVING -S IDE

5. A CTION 2: G OLF -S WING

6. A CTION 3: K ICKING

7. A CTION 5: R IDING -H ORSE

8. A CTION 6: R UN -S IDE

9. A CTION 7: S KATEBOARDING - FRONT

10. A CTION 9: S WING -S IDEANGLE

11. P LATT ’ S METHOD USING N EWTON ’ S METHOD WITH BACKTRACKING Lin et al. [2] propose an optimization to Platt’s Method [3] which uses Newton’s method with backtracking line search to solve for alpha and beta Some numerical difficulties with Platt’s Implementation are demonstrated alpha and beta are large p i is near zero or one

12. R EFERENCES [1] T. Malisiewicz, A. Gupta, and A. A. Efros. Ensemble of exemplar-SVMs for object detection and beyond. ICCV, 2011. [2] Hsuan-Tien Lin, Chih-Jen Lin, and Ruby C. Weng. A note on Platt's probabilistic outputs for Support Vector Machines. Mach. Learn., 68(3):267-276, October 2007. [3] John C. Platt. Probabilistic outputs for Support Vector Machines and comparisons to regularized likelihood methods. In Advances in large margin classifiers, pages 61-74. MIT Press, 1999.