ICCV2005: Contour-based approach for visual object recognition
Transcript of ICCV2005: Contour-based approach for visual object recognition
7/31/2019 ICCV2005: Contour-based approach for visual object recognition
http://slidepdf.com/reader/full/iccv2005-contour-based-approach-for-visual-object-recognition 1/23
Contour Based Approaches for
Visual Object Recognition
Jamie ShottonUniversity of Cambridge
Joint work with
Roberto Cipolla, Andrew Blake
7/31/2019 ICCV2005: Contour-based approach for visual object recognition
http://slidepdf.com/reader/full/iccv2005-contour-based-approach-for-visual-object-recognition 2/23
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
7/31/2019 ICCV2005: Contour-based approach for visual object recognition
http://slidepdf.com/reader/full/iccv2005-contour-based-approach-for-visual-object-recognition 3/23
Contour Features
Features contour fragments
and their parameters
Local features not whole contour account for variability separately
increase generalisation
decrease training requirements
7/31/2019 ICCV2005: Contour-based approach for visual object recognition
http://slidepdf.com/reader/full/iccv2005-contour-based-approach-for-visual-object-recognition 4/23
Object Model
p
σ
T
Model is set of M features star constellation
Each feature
contour fragmentexpected offsetmodel parametersclassifier parameters
7/31/2019 ICCV2005: Contour-based approach for visual object recognition
http://slidepdf.com/reader/full/iccv2005-contour-based-approach-for-visual-object-recognition 5/23
Matching Features
Canny
Edge
Detector
Distance
Transform
Gaussian weighted oriented chamfer matching
aligns features to image
7/31/2019 ICCV2005: Contour-based approach for visual object recognition
http://slidepdf.com/reader/full/iccv2005-contour-based-approach-for-visual-object-recognition 6/23
Matching Features
Gaussian weighted oriented chamfer matching
aligns features to image
Chamfer
Matching
feature match score at optimal position
optimal position
7/31/2019 ICCV2005: Contour-based approach for visual object recognition
http://slidepdf.com/reader/full/iccv2005-contour-based-approach-for-visual-object-recognition 7/23
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
7/31/2019 ICCV2005: Contour-based approach for visual object recognition
http://slidepdf.com/reader/full/iccv2005-contour-based-approach-for-visual-object-recognition 8/23
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
7/31/2019 ICCV2005: Contour-based approach for visual object recognition
http://slidepdf.com/reader/full/iccv2005-contour-based-approach-for-visual-object-recognition 9/23
Learning System
DetectionBoosting Algorithm K (c) SegmentedTraining Data
Test Data
Object
Detections
BackgroundTraining Data
7/31/2019 ICCV2005: Contour-based approach for visual object recognition
http://slidepdf.com/reader/full/iccv2005-contour-based-approach-for-visual-object-recognition 10/23
Training Data
ClassUnsegmented (40)
Segmented (10)
Background (50)
7/31/2019 ICCV2005: Contour-based approach for visual object recognition
http://slidepdf.com/reader/full/iccv2005-contour-based-approach-for-visual-object-recognition 11/23
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
7/31/2019 ICCV2005: Contour-based approach for visual object recognition
http://slidepdf.com/reader/full/iccv2005-contour-based-approach-for-visual-object-recognition 12/23
Learning System
DetectionBoosting Algorithm K 1(c) SegmentedTraining Data
UnsegmentedTraining Data
Detection
Object
Detections
Boosting
Algorithm K 2 (c)
Test Data
BackgroundTraining Data
Background
Training Data
7/31/2019 ICCV2005: Contour-based approach for visual object recognition
http://slidepdf.com/reader/full/iccv2005-contour-based-approach-for-visual-object-recognition 13/23
Building a Fragment Dictionary
… … Masks
(~10 images)
Contour
Fragments T n
(~1000 fragments)
… …
7/31/2019 ICCV2005: Contour-based approach for visual object recognition
http://slidepdf.com/reader/full/iccv2005-contour-based-approach-for-visual-object-recognition 14/23
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
7/31/2019 ICCV2005: Contour-based approach for visual object recognition
http://slidepdf.com/reader/full/iccv2005-contour-based-approach-for-visual-object-recognition 15/23
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
7/31/2019 ICCV2005: Contour-based approach for visual object recognition
http://slidepdf.com/reader/full/iccv2005-contour-based-approach-for-visual-object-recognition 16/23
Learning System
DetectionBoosting Algorithm K 1(c) SegmentedTraining Data
UnsegmentedTraining Data
Detection
Object
Detections
Boosting
Algorithm K 2 (c)
Test Data
BackgroundTraining Data
Background
Training Data
7/31/2019 ICCV2005: Contour-based approach for visual object recognition
http://slidepdf.com/reader/full/iccv2005-contour-based-approach-for-visual-object-recognition 17/23
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
7/31/2019 ICCV2005: Contour-based approach for visual object recognition
http://slidepdf.com/reader/full/iccv2005-contour-based-approach-for-visual-object-recognition 18/23
Contour Results
7/31/2019 ICCV2005: Contour-based approach for visual object recognition
http://slidepdf.com/reader/full/iccv2005-contour-based-approach-for-visual-object-recognition 19/23
Contour Results
7/31/2019 ICCV2005: Contour-based approach for visual object recognition
http://slidepdf.com/reader/full/iccv2005-contour-based-approach-for-visual-object-recognition 20/23
Contour Results
7/31/2019 ICCV2005: Contour-based approach for visual object recognition
http://slidepdf.com/reader/full/iccv2005-contour-based-approach-for-visual-object-recognition 21/23
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
7/31/2019 ICCV2005: Contour-based approach for visual object recognition
http://slidepdf.com/reader/full/iccv2005-contour-based-approach-for-visual-object-recognition 22/23
Contour Results
Occlusion Performance (horses) Performance of K 1
vs. K 2
(faces)
No. Segmented Training Images
7/31/2019 ICCV2005: Contour-based approach for visual object recognition
http://slidepdf.com/reader/full/iccv2005-contour-based-approach-for-visual-object-recognition 23/23
Conclusions
Contour is very powerful cue
Boot-strapping improves results
Future directions
extend to multiple classes, scales, views
segmentation