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Proportion Priors for Image Sequence Segmentation

Proportion Priors for Image Sequence Segmentation. Claudia Nieuwenhuis , etc. ICCV 2013 Oral. Motivation. Current algorithms about Image Sequence Segmentation Shape similarity Assumption : Rigid body transformation from similar viewpoint

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Proportion Priors for Image Sequence Segmentation

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  1. Proportion Priors for Image Sequence Segmentation Claudia Nieuwenhuis, etc. ICCV 2013 Oral

  2. Motivation • Current algorithms about Image Sequence Segmentation • Shape similarity • Assumption: Rigid body transformation from similar viewpoint • Reality: viewpoint changes, articulations or non-rigid deformation • Color similarity • Assumption: Similarity of color or feature distributions • Reality: Similar or overlapping color distributions between objects and background, Illumination changes • Other methods with relaxed assumptions? • Object subspaces or region correspondences, etc. • Problem: Optimization problems are complex and hard to solve.

  3. Contribution • What property of objects could be preserved among various images in a sequence? • Contributions: • Propose framework of proportional preserving priors, add ratio constraint to the classification model. • Construct a convex scheme to approximate it and calculate it efficiently. Invariant and robust to non-rigid deformation, articulation, illumination changes, color overlap Proportional information: Relative size of object parts, eg., size ratio of head to entire body

  4. Problem Definition • Bayesian inference for segmentation • : input image of a sequence on the domain • Task of segmentation: Partition the image plane into n pairwise disjoint regions • Compute a labeling Key point Observation likelihood: Color model learned from images

  5. Framework of Proportion Preserving Priors • Conditional independence assumption • Ratio constraint of one part to whole object: Background, Constant ratio constraint Short boundary length constraint

  6. Proportion Preserving Priors • Uniform Distribution Prior • Laplace Distribution Prior • Penalize deviations of the ratios from their median Advantage: simple and convex Weak point: not robust to outliers How to convert it into convex problem? See paper to get detail. Advantage: perform better and robust to outlier Weak point: not convex

  7. Results

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