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Part 4: combined segmentation and recognition

Part 4: combined segmentation and recognition. Li Fei-Fei. Aim. Given an image and object category, to segment the object. Object Category Model. Segmentation. Cow Image. Segmented Cow. Segmentation should (ideally) be shaped like the object e.g. cow-like

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Part 4: combined segmentation and recognition

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  1. Part 4: combined segmentation and recognition Li Fei-Fei

  2. Aim • Given an image and object category, to segment the object Object Category Model Segmentation Cow Image Segmented Cow • Segmentation should (ideally) be • shaped like the object e.g. cow-like • obtained efficiently in an unsupervised manner • able to handle self-occlusion

  3. In this section: brief paper reviews • Jigsaw approach: Borenstein & Ullman, 2001, 2002 • Concurrent recognition and segmentation: Yu and Shi, 2002 • Image parsing: Tu et al. 2003 • Interleaved segmentation: Liebe & Schiele, 2004, 2005 • OBJCUT: Kumar et al. 2005 • LOCUS: Winn and Jojic, 2005

  4. Jigsaw approach: Borenstein and Ullman, 2001, 2002

  5. Object-Specific Figure-Ground Segregation Stella X. Yu and Jianbo Shi, 2002

  6. Object-Specific Figure-Ground Segregation Some segmentation/detection results Yu and Shi, 2002

  7. Image parsing: Tu, Zhu and Yuille 2003

  8. Image parsing: Tu, Zhu and Yuille 2003

  9. Matched Codebook Entries Probabilistic Voting Interest Points Voting Space(continuous) Segmentation Backprojectionof Maxima Refined Hypotheses(uniform sampling) BackprojectedHypotheses Implicit Shape Model - Recognition Liebe and Schiele, 2003, 2005

  10. Cows: Results • Segmentations from interest points Single-frame recognition - No temporal continuity used! Liebe and Schiele, 2003, 2005

  11. OBJCUT:shape prior -- Layered Pictorial Structures (LPS) • Generative model • Composition of parts + spatial layout Layer 2 Spatial Layout (Pairwise Configuration) Layer 1 Parts in Layer 2 can occlude parts in Layer 1 Kumar, et al. 2004, 2005

  12. OBJCUT • Probability of labelling in addition has • Unary potential which depend on distance from Θ (shape parameter) Θ (shape parameter) Unary Potential Φx(mx|Θ) mx m(labels) my Object Category Specific MRF x y D(pixels) Image Plane Kumar, et al. 2004, 2005

  13. OBJCUT: Results Using LPS Model for Cow In the absence of a clear boundary between object and background Image Segmentation

  14. LOCUS model Shared between images Class shape π Class edge sprite μo,σo Deformation field D Position & size T Different for each image Mask m Edge image e Background appearance λ0 Object appearance λ1 Image Winn and Jojic, 2005

  15. Summary • Strength • Explains every pixel of the image • Useful for image editing, layering, etc. • Issues • Invariance issues • (especially) scale, view-point variations

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