Ivan Laptev IRISA/INRIA, Rennes, France September 07, 2006 Boosted Histograms for Improved Object Detection

Ivan Laptev

IRISA/INRIA, Rennes, France

September 07, 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

SVMNeural Networks

Too heavy

Possible approach:

Example 1:

Weak learnerWeak learner

1-dim. projections onto predefined vectors

Possible approach:

Example 2:

Weak learnerWeak learner

1-dim. projections onto predefined vectors

feature mean feature covariance

Fischer weak learnerFischer weak learner

Alternative approach:

Evidence from real image training data:

Fischer learner “1-bin” learner

• Assume Normal distribution of features (hopefully valid at least for some of ~10^5 features!)• Compute projection direction by FLD:

Histogram featuresHistogram features

~10^5 rectangle features

Histograms over 4 gradient orientations, 4 subdivisions for each reactangle

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




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:


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

examples


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

examples


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

examples


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


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


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:


• 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





Open questions:









Open questions:









Open questions:









Open questions:





• Detection tasks in VOC05,VOC06 are far from being solved, it is a challenge!

Documents

Ivan Laptev IRISA/INRIA, Rennes, France September 07, 2006 Boosted Histograms for Improved Object Detection