auto-context and its application to high-level vision tasks

Auto-Context and Its Application to High-level Vision Tasks

Zhuowen Tu CVPR 2008Presented by Vladimir Reilly

Problems Tackled in Paper Horse Segmentation

Label Every pixel in image as horse or background

Problems Tackled in Paper Image labeling

More complex segmentation

Problems Tackled in Paper Human body Segmentation

Label Body Parts

Solution Context ADABOOST

Cool Idea Contextual information is integrated directly into

ADABOOST Context not limited by spatial proximity Fast General

Context

Appearance Context

Label Context

?

Tree?

Grass?

Sky?

Human?

Grass

Previous Work CRFs

Previous Work Spatial Boost

In addition to appearance InformationLook at labels of neighbor pixels

Derive weak Spatial Learner

The Algorithm Iteration 1

Train Image Label Map

Extract 21x21patch

Generate Weak Appearance

Learners8000 possible features

Train Strong Classifier

The Algorithm Iteration > 1

Train Image Label Map

Segment Images

Probability Map

Extract 21x21patch

Generate Weak Appearance

Learners8000 possible features

Generate Weak Context Learners

Shoot RaysSample Along RaysCompute Statistics

4000 possible features

Probability out of adaboost

PBT

Results

Results

Google Images

Interesting Observations Starting with second classifier

90% of selected learners are context learners Label Context improves results Appearance Context worsens results

Probability Map

Train Image

Results

Results

Results