Learning Globally-Consistent Local Distance Functions for Shape-Based Image Retrieval and...
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Learning Globally-Consistent Local Distance Functions for Shape-Based Image Retrieval and Classification Computer Vision, 2007. ICCV 2007. IEEE 11th International
Learning Globally-Consistent Local Distance Functions for
Shape-Based Image Retrieval and Classification Computer Vision,
2007. ICCV 2007. IEEE 11th International Conference on Andrea
Frome, EECS, UC Berkeley Yoram Singer, Google, Inc Fei Sha, EECS,
UC Berkeley Jitendra Malik, EECS, UC Berkeley
Slide 2
Outline Introduction Training step Testing step Experiment
& Result Conclusion
Slide 3
Outline Introduction Training step Testing step Experiment
& Result Conclusion
Slide 4
What we do? Goal classify an image to a more appropriate
category Machine learning Two steps Training step Testing step
Slide 5
Outline Introduction Training step Testing step Experiment
& Result Conclusion
Slide 6
Flow chart: training Generate features each image from dataset,
ex: SIFT or geometric blur Input distances to SVM for training,
evaluate W Compute distance dji, dki
Slide 7
Flow chart: training Generate features each image from dataset,
ex: SIFT or geometric blur Input distances to SVM for training,
evaluate W Compute distance dji, dki
Slide 8
Choosing features Dataset: Caltech101 Patch-based Features SIFT
Old school Geometric Blur Its a notion of blurring The measure of
similarity between image patches The extension of Gaussian
blur
Slide 9
Geometric blur
Slide 10
Flow chart: training Generate features each image from dataset,
ex: SIFT or geometric blur Input distances to SVM for training,
evaluate W Compute distance dji, dki
Slide 11
Triplet dji is the distance from image j to i Its not
symmetric, ex: dji dij dki > dji djidki
Slide 12
How to compute distance L2 norm 1 2 3 dji, 1 m features dji, 1
distance vector dji Image j Image i
Slide 13
Example Given 101 category, 15 images each category 101*15
Feature j 101*15 distance vector Image j vs training data
Slide 14
Flow chart: training Generate features each image from dataset,
ex: SIFT or geometric blur Input distances to SVM for training,
evaluate W Compute distance dji, dki
Slide 15
Machine learning: SVM Support Vector Machine Function: Classify
prediction Supervised learning Training data are n dimension
vector
Slide 16
Example Male investigate Annual income Free time Have
girlfriend?
Slide 17
Ex: Training data
Slide 18
space free income vector
Slide 19
Slide 20
Mathematical expression(1/2)
Slide 21
Mathematical expression(2/2)
Slide 22
Support vector Model free income
Slide 23
But the world is not so ideal.
Slide 24
Real world data
Slide 25
Hyper-dimension
Slide 26
Error cut
Slide 27
SVM standard mathematical expression Trade-off
Slide 28
In this paper Goal: to get the weight vector W 101*15 feature
Image weight wj of W wj, 1 wj
Slide 29
Visualization of the weights
Slide 30
How to choose Triplets? Reference Image Good friend - In the
same class Bad friend - In the different class Ex: 101category, 15
images per category 14 good friends & 15*100(1500) bad friends
15*101(1515) reference images total of about 31.8 million
triplets
Slide 31
Mathematical expression(1/2) Idealistic: Scaling: Different:
The length of Weight i 00 triplet
Early stopping Satisfy KTT condition In mathematics, a solution
in nonlinear programming to be optimal.mathematicsnonlinear
programming Threshold Dual variable update falls below a value
Slide 36
Outline Introduction Training step Testing step Experiment
& Result Conclusion
Slide 37
Flow chart: testing Query an image i Output the most
appropriate category Calculate Dxi, x is all training data, except
itself.
Slide 38
Flow chart: testing Query an image i Output the most
appropriate category Calculate Dxi, x is all training data, except
itself.
Slide 39
Query image? Goal: classify the query image to an appropriate
class Using the remaining images in the dataset as the query
image
Slide 40
Flow chart: testing Query an image i Output the most
appropriate category Calculate Dxi, x is all training data, except
itself.
Slide 41
Distance function(1/2) Query image i Image i feature 101*15
distance vector Image i vs all training data dxi, 1
Slide 42
Distance function(2/2) 101*15 Image I vs all the training data
Dji
Slide 43
Flow chart: testing Query an image i Output the most
appropriate category Calculate Dxi, x is all training data, except
itself.
Slide 44
How to choose the best image? Modified 3-NN classifier no two
images agree on the class within the top 10 Take the class of the
top-ranked image of the 10
Slide 45
Outline Introduction Training step Testing step Experiment
& Result Conclusion
Slide 46
Experiment & Result Caltech 101 Feature Geometric blur
(shape feature) HSV histograms (color feature) 5, 10, 15, 20
training images per category
Slide 47
Slide 48
Confusion matrix for 15
Slide 49
Outline Introduction Training step Testing step Experiment
& Result Conclusion
Slide 50
Learning Globally-Consistent Local Distance Functions for
Shape-Based Image Retrieval and Classification