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

Motivation TrainTestTrainTest

Overview Image Scoring Mining Parts & Learning Templates

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

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

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

Model Initialization 1) randomly sample the positive training images for patch positions: 2) Initialize model parts: perfect case: worst case: 3) BoF features normalized 10 5 patches. 3) Prunning: remove unused parts

Learning k = 4

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

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

Qualitative Results

Willow Actions

Database of Human Attributes (HAT)

Stanford 40 Actions

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

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

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