problem: svm training is expensive – mining for hard negatives, bootstrapping solution: lda...
TRANSCRIPT
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Problem: SVM training is expensive– Mining for hard
negatives, bootstrapping
Solution: LDA (Linear Discriminant Analysis). – Extremely fast
training, very similar performance
Claim
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Linear Discriminant Analysis (LDA) Assumptions
Learning - Classification
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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.
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Implementation
Mean
Covariance
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Regularization
• Very large
• In my experiments 10, for making sure that is PSD.
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Covariance
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Fast training using LDA
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Use in clustering
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Clustering in WHO Space
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Clustering in WHO Space
HOG WHO
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Clustering in WHO Space
HOG WHO
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(a) SVM
Pedestrian DetectionLinear Discriminant Models
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SVM
LDA
Cen
Pedestrian DetectionLinear Discriminant Models
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Results
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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
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Results
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Pascal NN Classification
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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.