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Enhancing Object and Place Recognition through Context-Based Vision System

This study investigates the integration of contextual information for effective object and place recognition. It explores the superior capabilities of local and global image representations using techniques such as wavelet decomposition and PCA for dimensionality reduction. The approach employs a transition matrix, observation likelihood, and mixture of Gaussians to improve place recognition. Additionally, it examines the significance of context in object detection and localization by estimating object presence probabilities. A specially trained model for categories addresses novel places, enhancing recognition reliability in varied environments.

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Enhancing Object and Place Recognition through Context-Based Vision System

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Presentation Transcript

  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 • Count transitions + Dirichlet smoothing • Observation likelihood • Appearances: mixture of Gaussians • Uniform weights

  5. Influence of HMM

  6. Dealing with novel places • Separately trained model for categories

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

  8. Context for object localization • Coarse “expectation mask”

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