iccv2005: contour-based approach for visual object recognition

7/31/2019 ICCV2005: Contour-based approach for visual object recognition

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Contour Based Approaches for

Visual Object Recognition

Jamie ShottonUniversity of Cambridge

Joint work with

Roberto Cipolla, Andrew Blake



Contour-Based Learning

Goal – single class categorical recognition

learn to detect and localise objects

“find the car, face or horse”

How can we exploit object contour ?

Desired

detection

results

Our contribution



Contour Features

Features contour fragments

and their parameters

Local features not whole contour account for variability separately

increase generalisation

decrease training requirements



Object Model

p

σ

T

Model is set of M features star constellation

Each feature

contour fragmentexpected offsetmodel parametersclassifier parameters



Matching Features

Canny

Edge

Detector

Distance

Transform

Gaussian weighted oriented chamfer matching

aligns features to image



Matching Features

Gaussian weighted oriented chamfer matching

aligns features to image

Chamfer

Matching

feature match score at optimal position

optimal position



confidence weighted weak learner

Location Sensitive Classification

Feature match scores make detection simple

Detection uses a boosted classification function K (c):

M number of features

F m feature m

E canny edge map

c object centroid

match scorethresholded match score

m weak learner threshold

a m weak learner confidence

b m weak learner confidence

0-1 indicator function



Evaluate K (c) for all c gives a

classification map

confidence as function of

position

Globally thresholded local

maxima give final detections

Object Detection

test

image

classification

mapcontours

object

no object



Learning System

DetectionBoosting Algorithm K (c) SegmentedTraining Data

Test Data

Object

Detections

BackgroundTraining Data



Training Data

ClassUnsegmented (40)

Segmented (10)

Background (50)



Boot-Strapping

Learn detector K 1(c)

segmented training data

Evaluate detector K 1(c) on

unsegmented class images

locates object centroids

background images

locates clutter



Learning System

DetectionBoosting Algorithm K 1(c) SegmentedTraining Data

UnsegmentedTraining Data

Detection

Object

Detections

Boosting

Algorithm K 2 (c)

Test Data


Background

Training Data



Building a Fragment Dictionary

… … Masks

(~10 images)

Contour

Fragments T n

(~1000 fragments)

… …



Training Examples

Learn classifier K (c) by boosting from feature vectors x

target values y (object/background)

Encourage „good‟ classification map:

Take training examples at:

object

no object



Boosting as Feature Selection

Feature vectors

1000 random

fragments

50 discriminative

fragments

1. Fragment Selection

2. Model Parameter Estimation

Select , for each feature

3. Weak-Learner Estimation

Select , a , b for each feature

F k candidate feature

(fragment T 2 T,

parameters 2 , 2 )

N number of candidate features

= |T| x | | x | |

E i canny edge map I

c j example centroid j in image i



Learning System

DetectionBoosting Algorithm K 1(c) SegmentedTraining Data

UnsegmentedTraining Data

Detection

Object

Detections

Boosting

Algorithm K 2 (c)

Test Data


Background

Training Data



Contour Experiments

Datasets:

Weizmann Horses

UIUC Cars

Caltech Faces

Caltech Motorbikes

Caltech Background

Each category evaluated in turn

10 segmented training images

40 unsegmented training images

50 background images

single scale evaluation



Contour Results



Contour Results

Recall Precision equal error rates Weizmann Horses: 92.1%

UIUC Cars: 92.8%

Caltech Faces: 94.0%

Caltech Motorbikes: 92.4%

Horses Cars



Contour Results

Occlusion Performance (horses) Performance of K 1

vs. K 2

(faces)

No. Segmented Training Images



Conclusions

Contour is very powerful cue

Boot-strapping improves results

Future directions

extend to multiple classes, scales, views

segmentation

iccv2005: contour-based approach for visual object recognition

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