An Exemplar Model For Learning Object Classes
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![Page 1: An Exemplar Model For Learning Object Classes](https://reader033.fdocuments.net/reader033/viewer/2022052621/5586838dd8b42a02468b4729/html5/thumbnails/1.jpg)
An Exemplar Model for Learning Object Classes
Authors: Ondrej Chum Andrew Zisserman@University of Oxford
Presenter: Shao-Chuan Wang
![Page 2: An Exemplar Model For Learning Object Classes](https://reader033.fdocuments.net/reader033/viewer/2022052621/5586838dd8b42a02468b4729/html5/thumbnails/2.jpg)
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
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An Exemplar Model for Learning Object Classes
• Exemplar model:
• Detection (cost function):
X
eewwD
AYXdYXdC
2
2)()),((),(
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
ii
ii
yx
yxyxyxyxd
2222 )(
),(;),(),(
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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
X
ee
Y
wwL
AYXdYXdC
2
2)()),((),(
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Discriminative features
• Definition:
w
wwD
containingdatabaseinimage#
containingimageslabelledclass#~)(
Top 10 most discriminative visual words
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Constructing ROI exemplars: Algorithm
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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.)
![Page 8: An Exemplar Model For Learning Object Classes](https://reader033.fdocuments.net/reader033/viewer/2022052621/5586838dd8b42a02468b4729/html5/thumbnails/8.jpg)
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.)
![Page 9: An Exemplar Model For Learning Object Classes](https://reader033.fdocuments.net/reader033/viewer/2022052621/5586838dd8b42a02468b4729/html5/thumbnails/9.jpg)
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)()),((),(
![Page 10: An Exemplar Model For Learning Object Classes](https://reader033.fdocuments.net/reader033/viewer/2022052621/5586838dd8b42a02468b4729/html5/thumbnails/10.jpg)
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.)
![Page 11: An Exemplar Model For Learning Object Classes](https://reader033.fdocuments.net/reader033/viewer/2022052621/5586838dd8b42a02468b4729/html5/thumbnails/11.jpg)
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.)
![Page 12: An Exemplar Model For Learning Object Classes](https://reader033.fdocuments.net/reader033/viewer/2022052621/5586838dd8b42a02468b4729/html5/thumbnails/12.jpg)
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.)
![Page 13: An Exemplar Model For Learning Object Classes](https://reader033.fdocuments.net/reader033/viewer/2022052621/5586838dd8b42a02468b4729/html5/thumbnails/13.jpg)
Constructing ROI exemplars: Algorithm
• Three stages of the optimization process
Initialization
Optimization
Re-initializationviadetection
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Using the exemplar model
• Object Detection
X
eewwD
AYXdYXdC
2
2)()),((),(
),( iRwHypothesis
Clustering
w
nwDRwS Rw
#)(),( ),(
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
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Using other models
• Training:– Train an SVM, using features within ROI by
exemplar models• Object detection– Scores are ranked by SVM score
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Results
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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.