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 Combining Discriminative and Generative Models for Parsing Results Comments

Image Parsing Image I Parse Structure W Optimize p(W|I)

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 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 62 characters Faces Regions

Key Concepts: –Markov Chains –Markov Chain Monte Carlo Metropolis-Hastings [Metropolis 1953, Hastings 1970] Reversible Jump [Green 1995] –Data Driven Markov Chain Monte Carlo MCMC: A Quick Tour

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

Markov Chain Monte Carlo Notes: Slides by Zhu, Dellaert and Tu at ICCV 2005

Metropolis-Hastings Algorithm Notes: Slides by Zhu, Dellaert and Tu at ICCV 2005

Metropolis-Hastings Algorithm Proposal Distribution Invariant Distribution Notes: Slides by Zhu, Dellaert and Tu at ICCV 2005

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

DDMCMC Motivation Generative Model p(I|W)p(W) State Space

DDMCMC Motivation Generative Model p(I|W)p(W) State Space Discriminative Model q( w j | I ) Dramatically reduce search space by focusing sampling to highly probable states.

DDMCMC Framework Moves: –Node Creation –Node Deletion –Change Node Attributes

Transition Kernel Satisfies detailed balanced equation Full Transition Kernel

Convergence to p(W|I) Monotonically at a geometric rate

Criteria for Designing Transition Kernels

Image Generation Model Regions: Constant Intensity Textures Shading State of parse graph

62 characters Faces 3 Regions

Uniform Designed to penalize high model complexity

Shape Prior Faces 3 Regions

Shape Prior: Text

Intensity Models

Intensity Model: Faces

Discriminative Cues Used Adaboost Trained –Face Detector –Text Detector Adaptive Binarization Cues Edge Cues –Canny at 3 scales Shape Affinity Cues Region Affinity Cues

Transition Kernel Design Remember

Possible Transitions 1.Birth/Death of a Face Node 2.Birth/Death of Text Node 3.Boundary Evolution 4.Split/Merge Region 5.Change node attributes

Face/Text Transitions

Region Transitions

Change Node Attributes

Basic Control Algorithm

Results

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.

Thank You