ivan laptev irisa/inria, rennes, france december 08, 2006 boosted histograms for improved object...

Ivan Laptev

IRISA/INRIA, Rennes, France

December 08, 2006

Boosted HistogramsBoosted Histogramsfor for

Improved Object DetectionImproved Object Detection

• [Swain & Ballard 1991] - Color histograms

• [Schiele & Crowley 1996] - Receptive field histograms

• [Lowe 1999] - localized orientation histograms (SIFT)

• [Schneiderman & Kanade 2000] - localized histograms of wavelet coef.

• [Leung & Malik 2001] - Texton histograms

• [Belongie et.al. 2002] - Shape context

• [Dalal & Triggs 2005] - Dense orientation histograms

Remarkable success of recognition methods using histograms of local image measurements:

Likely explanation: Histograms are robust to image variations such as limited geometric transformations and object class variability.

Histograms for object recognitionHistograms for object recognition

Histograms

What to measure?

• No guarantee for optimal recognition • Different regions may have different discriminative power

Color

[SB91]

Gaussian derivatives

[SC96]

Wavelet coeff.

[SK00]

Textons

[LM01]

Gradient orientation

[L99,DT05]

Where to measure?

AB

C

DAB

C

D

Whole image

[SB91,SC96]

Pre-defined grid

[SK00,BMP02,DT05]

Key points

[L99]

Histograms: What vs. WhereHistograms: What vs. Where

• Efficient discriminative classifier [Freund&Schapire’97]• Good performance for face detection [Viola&Jones’01]

IdeaIdea

boosting

selected features

weak classifier

AdaBoost:

Haar features

Histogram features Fisher discriminant

optimal threshold

SVM, ANN

1-bin classifier

Histogram featuresHistogram features

~10^5 rectangle features

Histograms over 4 gradient orientations, 4 subdivisions for each rectangle

Training dataTraining data

Crop and resize

• Perturb annotation

• Increase training set X 10

+

Training: Selected FeaturesTraining: Selected Features

376 of ~10^5 features selected 0.999 correct classification10^-5 false positives

• Scan and classify image windows at different positions and scales

• Cluster detections in the space-scale space• Assign cluster size to the detection confidence

Conf.=5

Object detectionObject detection

motorbikes

bicycles

people

cars

#217 / #220

#123 / #123

#152 / #149

#320 / #341

PASCAL Visual Object ClassesPASCAL Visual Object ClassesChallenge 2005 (VOC’05)Challenge 2005 (VOC’05)

Ground truth annotation

Detection results:• >50 % overlap of bounding box with GT•one bounding box for each object• confidence value for each detection

Precision-Recall (PR) curve:

Average Precision (AP) value:

Evaluation criteriaEvaluation criteria

Detection results:• >50 % overlap of bounding box with GT•one bounding box for each object• confidence value for each detection

Detection results:• >50 % overlap of bounding box with GT•one bounding box for each object• confidence value for each detection

Detection results:• >50 % overlap of bounding box with GT•one bounding box for each object• confidence value for each detection

PR-curves for the “Motorbike” validation dataset:

[Levi and Weiss, CVPR 2004] “Learning object detection from a small number of examples: The importance of good features”

Evaluation of detectionEvaluation of detection

FLD learner

+ 1-bin classifier

Bicycles test1 People test1

cars test1Motorbikes test1

Results for VOC’05 ChallengeResults for VOC’05 Challenge

Average Precision values:

Results for VOC’05 ChallengeResults for VOC’05 Challenge

PASCAL Visual Object ClassesPASCAL Visual Object ClassesChallenge 2006 (VOC’06)Challenge 2006 (VOC’06)

examples

Results for VOC’06 ChallengeResults for VOC’06 Challenge

Competition "comp3" (train on VOC data) Class “bicycle"

examples

Results for VOC’06 ChallengeResults for VOC’06 Challenge

Competition "comp3" (train on VOC data) Class “cow"

examples

Results for VOC’06 ChallengeResults for VOC’06 Challenge

Competition "comp3" (train on VOC data) Class “horse"

Results for VOC’06 ChallengeResults for VOC’06 Challenge

Competition "comp3" (train on VOC data) Class “motorbike"

Results for VOC’06 ChallengeResults for VOC’06 Challenge

Competition "comp3" (train on VOC data) Class “person"

  bicycle bus car cat cow dog horse motorbike person sheep

Cambridge 0.249 0.138 0.254 0.151 0.149 0.118 0.091 0.178 0.030 0.131

ENSMP - - 0.398 - 0.159 - - - - -

INRIA_Douze 0.414 0.117 0.444 - 0.212 - - 0.390 0.164 0.251

INRIA_Laptev 0.440 - - - 0.224 - 0.140 0.318 0.114 -

TUD - - - - - - - 0.153 0.074 -

TKK 0.303 0.169 0.222 0.160 0.252 0.113 0.137 0.265 0.039 0.227

Average Precision values:

Results for VOC’06 ChallengeResults for VOC’06 Challenge

How “interesting” are boosted regions?How “interesting” are boosted regions?

Harris value is not a significant measure for boosted regions

Affine Harris regions

Boosted regions

“Interest regions” are not necessarily good for recognition?

• All results are obtained with a single set of parameters

• Small number of training samples is sufficient

• Efficient detection: 10fps on 320x280 images

• Extension to texton/color histogram features is straightforward

Open questions:

• Other free-shape regions better? How to find them?

• Better weak learner that takes advantage of histogram properties

• View transformations

Final NotesFinal Notes

• All results are obtained with a single set of parameters

• Small number of training samples is sufficient

• Efficient detection: 10fps on 320x280 images

• Extension to texton/color histogram features is straightforward

Open questions:

• Other free-shape regions better? How to find them?

• Better weak learner that takes advantage of histogram properties

• View transformations

Final NotesFinal Notes

• All results are obtained with a single set of parameters

• Small number of training samples is sufficient

• Efficient detection: 10fps on 320x280 images

• Extension to texton/color histogram features is straightforward

Open questions:

• Other free-shape regions better? How to find them?

• Better weak learner that takes advantage of histogram properties

• View transformations

Final NotesFinal Notes

• All results are obtained with a single set of parameters

• Small number of training samples is sufficient

• Efficient detection: 10fps on 320x280 images

• Extension to texton/color histogram features is straightforward

Open questions:

• Other free-shape regions better? How to find them?

• Better weak learner that takes advantage of histogram properties

• View transformations

Final NotesFinal Notes

• All results are obtained with a single set of parameters

• Small number of training samples is sufficient

• Efficient detection: 10fps on 320x280 images

• Extension to texton/color histogram features is straightforward

Open questions:

• Other free-shape regions better? How to find them?

• Better weak learner that takes advantage of histogram properties

• View transformations

Final NotesFinal Notes