Introduction

2. Introduction Problem: Classifying attributes and actions in still images Model: • Collection of part templates • Specific scale space locations (human centric) • Discriminative learning • Sparse Activation

3. Motivation Train Test Train Test

4. Overview Mining Parts & Learning Templates Image Scoring

5. Formulation Dataset: Model: fractional multiples of width and height Objective:

6. Model fractional multiples of width and height d = 1000 . . . Model Part 1 Part 2 Part 3 parts

7. Model & Scoring Image Scoring Model Optimization: Greedy selection of 0.33 overlap constraint overlap constraint sparse activation

8. Model Initialization 1) randomly sample the positive training images for patch positions: 2) Initialize model parts: perfect case: worst case: 3) BoF features normalized 105patches. 3) Prunning: remove unused parts

9. Learning k = 4

10. Experiments Willow 7 Human actions 27 Human Attributes (HAT) Stanford 40 Human Actions

11. Implementation Features: • VLFeat - Dense SIFT, • step size: 4 pixels • square patches (8 to 40 pixels) • k-means - vocabulary 1000 • explicit feature map + Bhattacharyya (Hellinger – Square root) kernel Baseline: 4 level spatial pyramid Immediate context: • expand the human bounding boxes by 50% in both width and height Full image context: • full image classifier uses 4 level SPM with an exponential 2 kernel

12. Qualitative Results

13. Willow Actions

14. Database of Human Attributes (HAT)

15. Stanford 40 Actions

16. Learned Parts - I In each row, the first image is the patch used to initialize the part and the remaining images are its top scoring patches

17. Learned Parts - II In each row, the first image is the patch used to initialize the part and the remaining images are its top scoring patches

18. Learned Parts - III In each row, the first image is the patch used to initialize the part and the remaining images are its top scoring patches