Cvpr2007 object category recognition p4 - combined segmentation and recognition

Part 4: Combined segmentation and recognition by Rob Fergus (MIT)

Aim ,[object Object],[object Object],[object Object],[object Object],[object Object],Segmentation Object Category Model Cow Image Segmented Cow Slide from Kumar ‘05

Examples of bottom-up segmentation ,[object Object],Borenstein and Ullman, ECCV 2002

Jigsaw approach: Borenstein and Ullman, 2002

Implicit Shape Model - Liebe and Schiele, 2003 Liebe and Schiele, 2003, 2005 Backprojected Hypotheses Interest Points Matched Codebook Entries Probabilistic Voting Voting Space (continuous) Backprojection of Maxima Segmentation Refined Hypotheses (uniform sampling)

Random Fields for segmentation I = Image pixels (observed) h = foreground/background labels (hidden) – one label per pixel  = Parameters Prior Likelihood Posterior Joint ,[object Object],[object Object],[object Object],[object Object]

Generative Markov Random Field I (pixels) Image Plane i j Prior has no dependency on I h (labels)  {foreground,background} h i h j Unary Potential  i ( I |h i ,  i ) Pairwise Potential (MRF)  ij (h i , h j |  ij ) MRF Prior Likelihood

Conditional Random Field Lafferty, McCallum and Pereira 2001 Pairwise Unary ,[object Object],[object Object],Discriminative approach e.g Kumar and Hebert 2003 I (pixels) Image Plane i j h i h j

OBJCUT Ω (shape parameter) Kumar, Torr & Zisserman 2005 Pairwise Unary ,[object Object],[object Object],[object Object],[object Object],[object Object],Label smoothness Contrast Distance from Ω Color Likelihood I (pixels) Image Plane i j h i h j Figure from Kumar et al., CVPR 2005

OBJCUT: Shape prior - Ω - Layered Pictorial Structures (LPS) ,[object Object],[object Object],Layer 2 Layer 1 Parts in Layer 2 can occlude parts in Layer 1 Spatial Layout (Pairwise Configuration) Kumar, et al. 2004, 2005

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

Levin & Weiss [ECCV 2006] Segmentation alignment with image edges Consistency with fragments segmentation Resulting min-cut segmentation

Winn and Shotton 2006 Layout Consistent Random Field [Lepetit et al. CVPR 2005] ,[object Object],[object Object],Classifier

Layout consistency Neighboring pixels (p,q) ? (p,q+1) (p,q) (p+1,q+1) (p-1,q+1) Layout consistent Winn and Shotton 2006 (8,3) (9,3) (7,3) (8,2) (9,2) (7,2) (8,4) (9,4) (7,4)

Layout Consistent Random Field Winn and Shotton 2006 Layout consistency Part detector

Stability of part labelling Part color key

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

Image parsing: Tu, Zhu and Yuille 2003

Segment out all the cars … . fused tree model for cars Unseen image Training images Segmented Cars Segmentation Trees Overview Multiscale Seg. Todorovic and Ahuja, CVPR 2006 Slide from T. Wu

LOCUS model Deformation field D Position & size T Class shape π Class edge sprite μ o , σ o Edge image e Image Object appearance λ 1 Background appearance λ 0 Mask m Shared between images Different for each image Kannan, Jojic and Frey 2004 Winn and Jojic, 2005

In this section: brief paper reviews ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Summary ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Conditional Random Fields for Segmentation ,[object Object],[object Object],Low-level pairwise term High-level local term Pixel-wise similarity

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

[object Object],[object Object],[object Object],[object Object],[object Object]

OBJCUT ,[object Object],[object Object],D (pixels) m (labels) Θ (shape parameter) Image Plane Object Category Specific MRF x y m x m y Unary Potential Φ x (m x | Θ ) Kumar, et al. 2004, 2005

Levin and Weiss 2006 Levin and Weiss, ECCV 2006

Cows: Results ,[object Object],[object Object],Liebe and Schiele, 2003, 2005

Examples of low-level image segmentation ,[object Object],Borenstein & Ullman, ECCV 2002

Jigsaw approach ,[object Object]

Segmentation ,[object Object],[object Object],[object Object],Liebe and Schiele, 2003, 2005 p(figure) p(ground) Segmentation p(figure) p(ground) Original image

Cvpr2007 object category recognition p4 - combined segmentation and recognition

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