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Image Parsing: Unifying Segmentation and Detection. Z. Tu, X. Chen, A.L. Yuille and S-C. Hz ICCV 2003 (Marr Prize) & IJCV 2005 Sanketh Shetty. Outline. Why Image Parsing? Introduction to Concepts in DDMCMC DDMCMC applied to Image Parsing

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image parsing unifying segmentation and detection

Image Parsing: Unifying Segmentation and Detection

Z. Tu, X. Chen, A.L. Yuille and S-C. Hz

ICCV 2003 (Marr Prize) & IJCV 2005

Sanketh Shetty

outline
Outline
  • Why Image Parsing?
  • Introduction to Concepts in DDMCMC
  • DDMCMC applied to Image Parsing
  • Combining Discriminative and Generative Models for Parsing
  • Results
  • Comments
image parsing
Image Parsing

Optimize p(W|I)

Image I

Parse Structure W

properties of parse structure
Properties of Parse Structure
  • Dynamic and reconfigurable
    • Variable number of nodes and node types
  • Defined by a Markov Chain
    • Data Driven Markov Chain Monte Carlo (earlier work in segmentation, grouping and recognition)
key concepts
Key Concepts
  • Joint model for Segmentation & Recognition
    • Combine different modules to obtain cues
  • Fully generative explanation for Image generation
    • Uses Generative and Discriminative Models + DDMCMC framework
    • Concurrent Top-Down & Bottom-Up Parsing
pattern classes
Pattern Classes

62 characters

Faces

Regions

mcmc a quick tour
MCMC: A Quick Tour
  • Key Concepts:
    • Markov Chains
    • Markov Chain Monte Carlo
      • Metropolis-Hastings [Metropolis 1953, Hastings 1970]
      • Reversible Jump [Green 1995]
    • Data Driven Markov Chain Monte Carlo
markov chains
Markov Chains

Notes: Slides by Zhu, Dellaert and Tu at ICCV 2005

markov chain monte carlo
Markov Chain Monte Carlo

Notes: Slides by Zhu, Dellaert and Tu at ICCV 2005

metropolis hastings algorithm
Metropolis-Hastings Algorithm

Notes: Slides by Zhu, Dellaert and Tu at ICCV 2005

metropolis hastings algorithm1
Metropolis-Hastings Algorithm

Invariant Distribution

Proposal Distribution

Notes: Slides by Zhu, Dellaert and Tu at ICCV 2005

reversible jumps mcmc
Reversible Jumps MCMC
  • Many competing models to explain data
    • Need to explore this complicated state space

Notes: Slides by Zhu, Dellaert and Tu at ICCV 2005

ddmcmc motivation
DDMCMC Motivation

Unifies

Notes: Slides by Zhu, Dellaert and Tu at ICCV 2005

ddmcmc motivation1
DDMCMC Motivation

Generative Model

p(I|W)p(W)

State Space

ddmcmc motivation2
DDMCMC Motivation

Generative Model

p(I|W)p(W)

State Space

Discriminative Model

q( wj| I )

Dramatically reduce search space by focusing

sampling to highly probable states.

ddmcmc framework
DDMCMC Framework
  • Moves:
    • Node Creation
    • Node Deletion
    • Change Node Attributes
transition kernel
Transition Kernel

Satisfies detailed balanced equation

Full Transition Kernel

convergence to p w i
Convergence to p(W|I)

Monotonically at a geometric rate

image generation model
Image Generation Model

Regions:

Constant Intensity

Textures

Shading

State of parse graph

slide21

62 characters

Faces

3 Regions

slide22

Uniform

Designed to penalize high model complexity

shape prior
Shape Prior

Faces

3 Regions

discriminative cues used
Discriminative Cues Used
  • Adaboost Trained
    • Face Detector
    • Text Detector
  • Adaptive Binarization Cues
  • Edge Cues
    • Canny at 3 scales
  • Shape Affinity Cues
  • Region Affinity Cues
possible transitions
Possible Transitions
  • Birth/Death of a Face Node
  • Birth/Death of Text Node
  • Boundary Evolution
  • Split/Merge Region
  • Change node attributes
comments
Comments
  • Well motivated but very complicated approach to THE HOLY GRAIL problem in vision
    • Good global convergence results for inference with very minor dependence on initial W.
    • Extensible to larger set of primitives and pattern types.
  • Many details of the algorithm are missing and it is hard to understand the motivation for choices of values for some parameters
  • Unclear if the p(W|I)’s for configurations with different class compositions are comparable.
  • Derek’s comment on Adaboost false positives and their failure to report their exact improvement
  • No quantitative results/comparison to other algorithms and approaches
    • It should be possible to design a simple experiment to measure performance on recognition/detection/localization tasks.
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