1. Context-based vision system for place and object recognition
Torralba, Murphy, Freeman & Rubin (2003)

2. Context is useful for object recognition

3. Local and global image representations
Local representation (L)
Wavelet decomposition
N = 24 (6 orientations, 4 scales)
Global representation
Average across space and downsample to M x M (M = 4)
PCA to reduce to 80 dimensions

4. Place recognition Transition matrix Observation likelihood
Count transitions + Dirichlet smoothing
Observation likelihood
Appearances: mixture of Gaussians
Uniform weights

6. Influence of HMM

7. Dealing with novel places
Separately trained model for categories

9. Using context for object detection
Estimate probability of object presence using global image features
(Objects independent given location and image features)

10. Context for object localization
Coarse “expectation mask”