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Problem : SVM training is expensive Mining for hard negatives, bootstrapping

Date post: 22-Feb-2016
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Claim. Problem : SVM training is expensive Mining for hard negatives, bootstrapping Solution : LDA (Linear Discriminant Analysis). Extremely fast training, very similar performance. Linear Discriminant Analysis ( LDA) . Assumptions. Learning - Classification. Implementation. - PowerPoint PPT Presentation
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Page 1: Problem :  SVM training is  expensive Mining  for hard negatives, bootstrapping
Page 2: Problem :  SVM training is  expensive Mining  for hard negatives, bootstrapping

Problem: SVM training is expensive– Mining for hard

negatives, bootstrapping

Solution: LDA (Linear Discriminant Analysis). – Extremely fast

training, very similar performance

Claim

Page 3: Problem :  SVM training is  expensive Mining  for hard negatives, bootstrapping

Linear Discriminant Analysis (LDA) Assumptions

Learning - Classification

Page 4: Problem :  SVM training is  expensive Mining  for hard negatives, bootstrapping

ImplementationFeatures

a simple procedure that allows us to learn a and a (corresponding to the background) once, and then reuse it for every window size N and for every object category.

Page 5: Problem :  SVM training is  expensive Mining  for hard negatives, bootstrapping

Implementation

Mean

Covariance

Page 6: Problem :  SVM training is  expensive Mining  for hard negatives, bootstrapping

Regularization

• Very large

• In my experiments 10, for making sure that is PSD.

Page 7: Problem :  SVM training is  expensive Mining  for hard negatives, bootstrapping

Covariance

Page 8: Problem :  SVM training is  expensive Mining  for hard negatives, bootstrapping

Fast training using LDA

Page 9: Problem :  SVM training is  expensive Mining  for hard negatives, bootstrapping

Use in clustering

Page 10: Problem :  SVM training is  expensive Mining  for hard negatives, bootstrapping

Clustering in WHO Space

Page 11: Problem :  SVM training is  expensive Mining  for hard negatives, bootstrapping

Clustering in WHO Space

HOG WHO

Page 12: Problem :  SVM training is  expensive Mining  for hard negatives, bootstrapping

Clustering in WHO Space

HOG WHO

Page 13: Problem :  SVM training is  expensive Mining  for hard negatives, bootstrapping

(a) SVM

Pedestrian DetectionLinear Discriminant Models

Page 14: Problem :  SVM training is  expensive Mining  for hard negatives, bootstrapping

SVM

LDA

Cen

Pedestrian DetectionLinear Discriminant Models

Page 15: Problem :  SVM training is  expensive Mining  for hard negatives, bootstrapping

Results

Page 16: Problem :  SVM training is  expensive Mining  for hard negatives, bootstrapping

Results

Method Mean AP Train complexity

Test complexity

ESVM + Co-occ 22.6 High High

ESVM + Calibr 19.8 High High

ELDA + Calibr 19.1 Low High

Ours full 21.0 Low Low

Page 17: Problem :  SVM training is  expensive Mining  for hard negatives, bootstrapping

Results

Page 18: Problem :  SVM training is  expensive Mining  for hard negatives, bootstrapping

Pascal NN Classification

Page 19: Problem :  SVM training is  expensive Mining  for hard negatives, bootstrapping

Summary• Whitened for HOG is better than HOG

• LDA for fast training of hog templates– Object Independent Background (?)

• mean better represents the cluster compared to the medoid– Use all the samples rather than 1

• Their statistical models also suggest that natural image statistics, largely ignored in the field of object detection, are worth (re)visiting.


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