Context-based vision system for place and object recognition

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Presentation transcript:

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

Context is useful for object recognition

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

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

Influence of HMM

Dealing with novel places Separately trained model for categories

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

Context for object localization Coarse “expectation mask”