- 115 Views
- Uploaded on

Download Presentation
## PowerPoint Slideshow about ' Image Parsing: Unifying Segmentation and Detection' - javier

**An Image/Link below is provided (as is) to download presentation**

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript

### 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

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

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

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

Invariant Distribution

Proposal 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

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

- Moves:
- Node Creation
- Node Deletion
- Change Node Attributes

Convergence to p(W|I)

Monotonically at a geometric rate

Designed to penalize high model complexity

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

- Birth/Death of a Face Node
- Birth/Death of Text Node
- Boundary Evolution
- Split/Merge Region
- Change node attributes

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.

Download Presentation

Connecting to Server..