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

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 2 Part 3

parts

d = 1000 Model

Model & Scoring

Image ScoringModel

overlap constraintsparse activationOptimization: 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 105 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 initializethe part and the remaining images are its top scoring patches

Learned Parts - II

In each row, the first image is the patch used to initializethe part and the remaining images are its top scoring patches

Learned Parts - III

In each row, the first image is the patch used to initializethe part and the remaining images are its top scoring patches