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

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.

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

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  1. Claim Problem: SVM training is expensive • Mining for hard negatives, bootstrapping Solution: LDA (Linear Discriminant Analysis). • Extremely fast training, very similar performance

  2. Linear Discriminant Analysis (LDA) Assumptions Learning - Classification

  3. Implementation Features 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.

  4. Implementation Mean Covariance

  5. Regularization • Very large • In my experiments 10, for making sure that is PSD.

  6. Covariance

  7. Fast training using LDA

  8. Use in clustering

  9. Clustering in WHO Space

  10. Clustering in WHO Space WHO HOG

  11. Clustering in WHO Space WHO HOG

  12. Pedestrian DetectionLinear Discriminant Models (a) SVM

  13. Pedestrian DetectionLinear Discriminant Models SVM LDA Cen

  14. Results

  15. Results

  16. Results

  17. Pascal NN Classification

  18. 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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