Post on 21-Jun-2015
An Exemplar Model for Learning Object Classes
Authors: Ondrej Chum Andrew Zisserman@University of Oxford
Presenter: Shao-Chuan Wang
An Exemplar Model for Learning Object Classes
• Objective:– Give training images known to contain instances of an
object class, without specifying locations and scales.– Detect and localize object
• Kea Ideas: – Learn region of interest (ROI) around class instance in
weakly supervised training data.– Based on discriminative features to initialize ROI for
the optimization problem
An Exemplar Model for Learning Object Classes
• Exemplar model:
• Detection (cost function):
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X: exemplar setX^w: PHOW descriptorX^e: PHOG descriptorA: aspect ratio of target region
XY
d: distance function/mu: mean of exemplars’ aspect ratio/sigma: std of exemplars’ aspect ratio/alpha, /beta: weighting to be tuned/learned
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An Exemplar Model for Learning Object Classes
• Learning the exemplar model:– Learn the regions in all images simultaneously.
• How to Determine initial ROI?– > By discriminative features
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Discriminative features
• Definition:
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containingdatabaseinimage#
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Top 10 most discriminative visual words
Constructing ROI exemplars: Algorithm
Constructing ROI exemplars: Algorithm
1. Initialization– Calculate discriminability of visual words– Initialize the ROI in each training image by a bounding box of the
64 most discriminative features
2. Optimization of cost function– Find the ROI to minimize the cost function with \beta = 0– Re-initialization by detection
3. Refinement– Enlarge the ROI in the training images by 10%– Calculate discriminability of visual words using only the features
inside the ROI– Optimization of cost function (goto 2.)
Constructing ROI exemplars: Algorithm
1. Initialization– Calculate discriminability of visual words– Initialize the ROI in each training image by a bounding box of the
64 most discriminative features
2. Optimization of cost function– Find the ROI to minimize the cost function with \beta = 0– Re-initialization by detection
3. Refinement– Enlarge the ROI in the training images by 10%– Calculate discriminability of visual words using only the features
inside the ROI– Optimization of cost function (goto 2.)
Constructing ROI exemplars: Algorithm
1. Initialization– Calculate discriminability of visual words– Initialize the ROI in each training image by a bounding box of the
64 most discriminative features
2. Optimization of cost function– Find the ROI to minimize the cost function with \beta = 0– Re-initialization by detection
3. Refinement– Enlarge the ROI in the training images by 10%– Calculate discriminability of visual words using only the features
inside the ROI– Optimization of cost function (goto 2.)
X
ee
Y
wwL
AYXdYXdC
2
2)()),((),(
Constructing ROI exemplars: Algorithm
1. Initialization– Calculate discriminability of visual words– Initialize the ROI in each training image by a bounding box of the
64 most discriminative features
2. Optimization of cost function– Find the ROI to minimize the cost function with \beta = 0– Re-initialization by detection.
3. Refinement– Enlarge the ROI in the training images by 10%– Calculate discriminability of visual words using only the features
inside the ROI– Optimization of cost function (goto 2.)
Constructing ROI exemplars: Algorithm
1. Initialization– Calculate discriminability of visual words– Initialize the ROI in each training image by a bounding box of the
64 most discriminative features
2. Optimization of cost function– Find the ROI to minimize the cost function with \beta = 0– Re-initialization by detection.
3. Refinement– Enlarge the ROI in the training images by 10%– Calculate discriminability of visual words using only the features
inside the ROI– Optimization of cost function (goto 2.)
Constructing ROI exemplars: Algorithm
1. Initialization– Calculate discriminability of visual words– Initialize the ROI in each training image by a bounding box of the
64 most discriminative features
2. Optimization of cost function– Find the ROI to minimize the cost function with \beta = 0– Re-initialization by detection.
3. Refinement– Enlarge the ROI in the training images by 10%– Calculate discriminability of visual words using only the features
inside the ROI– Optimization of cost function (goto 2.)
Constructing ROI exemplars: Algorithm
• Three stages of the optimization process
Initialization
Optimization
Re-initializationviadetection
Using the exemplar model
• Object Detection
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),( iRwHypothesis
Clustering
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Score of a hypothesis
n_(w,R): the number of exemplar Images consistent with the hypothesis
#w: the number of appearances of the visual word w in the exemplar images
20 strongest hypotheses are tested on each test image
Using other models
• Training:– Train an SVM, using features within ROI by
exemplar models• Object detection– Scores are ranked by SVM score
Results
Conclusion
• When constructing exemplars’ ROI, they use discriminability to initialize bounding box
• In detection, they used relative position of bounding boxes and visual words to try the most probable hypotheses.
• It may failed to detect when significant class variability in the exemplars, such as people class.