Learning to Combine Bottom-Up and Top-Down Segmentation Anat

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Learning to Combine Bottom-Up and Top-Down Segmentation Anat Levin and Yair Weiss School of Learning to Combine Bottom-Up and Top-Down Segmentation Anat Levin and Yair Weiss School of CS&Eng, The Hebrew University of Jerusalem, Israel

Bottom-up segmentation Bottom-up approaches: Use low level cues to group similar pixels • Malik Bottom-up segmentation Bottom-up approaches: Use low level cues to group similar pixels • Malik et al, 2000 • Sharon et al, 2001 • Comaniciu and Meer, 2002 • …

Bottom-up segmentation is ill posed Many possible segmentation are equally good based on low Bottom-up segmentation is ill posed Many possible segmentation are equally good based on low level cues alone. Some segmentation example (maybe horses from Eran’s paper) images from Borenstein and Ullman 02

Top-down segmentation • Class-specific, top-down segmentation (Borenstein & Ullman Eccv 02) • Winn and Top-down segmentation • Class-specific, top-down segmentation (Borenstein & Ullman Eccv 02) • Winn and Jojic 05 • Leibe et al 04 • Yuille and Hallinan 02. • Liu and Sclaroff 01 • Yu and Shi 03

Combining top-down and bottom-up segmentation + Find a segmentation: 1. Similar to the top-down Combining top-down and bottom-up segmentation + Find a segmentation: 1. Similar to the top-down model 2. Aligns with image edges

Previous approaches • Borenstein et al 04 Combining top-down and bottom up segmentation. • Previous approaches • Borenstein et al 04 Combining top-down and bottom up segmentation. • Tu et al ICCV 03 Image parsing: segmentation, detection, and recognition. • Kumar et al CVPR 05 Obj-Cut. • Shotton et al ECCV 06: Texton. Boost Previous approaches: Train top-down and bottom-up models independently

Why learning top-down and bottom-up models simultaneously? • Large number of freedom degrees in Why learning top-down and bottom-up models simultaneously? • Large number of freedom degrees in tentacles configuration- requires a complex deformable top down model • On the other hand: rather uniform colors- low level segmentation is easy

Our approach • Learn top-down and bottom-up models simultaneously • Reduces at run time Our approach • Learn top-down and bottom-up models simultaneously • Reduces at run time to energy minimization with binary labels (graph min cut)

Energy model Segmentation alignment with image edges Consistency with fragments segmentation Energy model Segmentation alignment with image edges Consistency with fragments segmentation

Energy model Segmentation alignment with image edges Consistency with fragments segmentation Energy model Segmentation alignment with image edges Consistency with fragments segmentation

Energy model Segmentation alignment with image edges Consistency with fragments segmentation Resulting min-cut segmentation Energy model Segmentation alignment with image edges Consistency with fragments segmentation Resulting min-cut segmentation

Learning from segmented class images Training data: Goal: Learn fragments for an energy function Learning from segmented class images Training data: Goal: Learn fragments for an energy function

Learning energy functions using conditional random fields Theory of CRFs: CRFs For vision: • Learning energy functions using conditional random fields Theory of CRFs: CRFs For vision: • Lafferty et al 2001 • Kumar and Hebert 2003 • Le. Cun and Huang 2005 • Ren et al 2006 • He et al 2004, 2006 • Quattoni et al 2005 • Torralba et al 04

Learning energy functions using conditional random fields Minimize energy of true segmentation E(x) Maximize Learning energy functions using conditional random fields Minimize energy of true segmentation E(x) Maximize energy of all other configurations “It's not enough to succeed. Others must fail. ” –Gore Vidal

Learning energy functions using conditional random fields Minimize energy of true segmentation P(x) Maximize Learning energy functions using conditional random fields Minimize energy of true segmentation P(x) Maximize energy of all other configurations “It's not enough to succeed. Others must fail. ” –Gore Vidal

Differentiating CRFs log-likelihood Log-likelihood is convex with respect to Log-likelihood gradients with respect to Differentiating CRFs log-likelihood Log-likelihood is convex with respect to Log-likelihood gradients with respect to : Expected feature response minus observed feature response

Conditional random fields-computational challenges CRFs cost- evaluating partition function Derivatives- evaluating marginal probabilities Use Conditional random fields-computational challenges CRFs cost- evaluating partition function Derivatives- evaluating marginal probabilities Use approximate estimations: • Sampling • Belief Propagation and Bethe free energy • Used in this work: Tree reweighted belief propagation and Tree reweighted upper bound (Wainwright et al 03)

Fragments selection Candidate fragments pool: Greedy energy design: Fragments selection Candidate fragments pool: Greedy energy design:

Fragments selection challenges Straightforward computation of likelihood improvement is impractical 2000 Fragments 50 Training Fragments selection challenges Straightforward computation of likelihood improvement is impractical 2000 Fragments 50 Training images 10 Fragments selection iterations 1, 000 inference operations!

Fragments selection First order approximation to log-likelihood gain: Fragment with low error on the Fragments selection First order approximation to log-likelihood gain: Fragment with low error on the training set Similar idea in different contexts: • Zhu et al 1997 • Lafferty et al 2004 • Mc. Callum 2003 Fragment not accounted for by the existing model

Fragments selection First order approximation to log-likelihood gain: • Requires a single inference process Fragments selection First order approximation to log-likelihood gain: • Requires a single inference process on the previous iteration energy to evaluate approximations with respect to all fragments • First order approximation evaluation is linear in the fragment size

Fragments selection- summary Initialization: Low- level term For k=1: K • Run TRBP inference Fragments selection- summary Initialization: Low- level term For k=1: K • Run TRBP inference using the previous iteration energy. • Approximate likelihood gain of candidate fragments • Add to energy the fragment with maximal gain.

Training horses model Training horses model

Training horses model-one fragment Training horses model-one fragment

Training horses model-two fragments Training horses model-two fragments

Training horses model-three fragments Training horses model-three fragments

Results- horses dataset Results- horses dataset

Mislabeled pixels percent Results- horses dataset Fragments number Comparable to previous results (Kumar et Mislabeled pixels percent Results- horses dataset Fragments number Comparable to previous results (Kumar et al, Borenstein et al. ) but with far fewer fragments

Results- artificial octopi Results- artificial octopi

Results- cows dataset From the TU Darmstadt Database Results- cows dataset From the TU Darmstadt Database

Mislabeled pixels percent Results- cows dataset Fragments number Mislabeled pixels percent Results- cows dataset Fragments number

Conclusions • Simultaneously learning top-down and bottom-up segmentation cues. • Learning formulated as estimation Conclusions • Simultaneously learning top-down and bottom-up segmentation cues. • Learning formulated as estimation in Conditional Random Fields • Novel, efficient fragments selection algorithm • Algorithm achieves state of the art performance with a significantly smaller number of fragments




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