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Advanced Techniques in Combined Segmentation and Recognition in Images

This section explores the integration of segmentation and recognition methods aimed at segmenting specific object categories, such as cows, from images. The objective is to develop efficient, unsupervised techniques that create object-shaped segments and can manage difficulties like self-occlusion. Key approaches reviewed include the Jigsaw method, concurrent recognition and segmentation, and image parsing strategies. Additionally, examples of the application and results of these methods are discussed, highlighting both strengths and potential issues in real-world scenarios.

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Advanced Techniques in Combined Segmentation and Recognition in Images

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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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