ivan laptev irisa/inria, rennes, france september 07, 2006
DESCRIPTION
Boosted Histograms for Improved Object Detection. Ivan Laptev IRISA/INRIA, Rennes, France September 07, 2006. Histograms for object recognition. Remarkable success of recognition methods using histograms of local image measurements:. [Swain & Ballard 1991] - Color histograms - PowerPoint PPT PresentationTRANSCRIPT
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
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
• 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
• Detection tasks in VOC05,VOC06 are far from being solved, it is a challenge!