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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. Train. Test. Train. Test. Overview. Mining Parts &
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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 Train Test Train Test
Overview Mining Parts & Learning Templates Image Scoring
Formulation Dataset: Model: fractional multiples of width and height Objective:
Model fractional multiples of width and height d = 1000 . . . Model Part 1 Part 2 Part 3 parts
Model & Scoring Image Scoring Model Optimization: Greedy selection of 0.33 overlap constraint overlap constraint sparse activation
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
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
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