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ECCV2010: feature learning for image classification, part 3
1. Part 3: Image Classification using Sparse Coding: Advanced Topics Kai Yu Dept. of Media Analytics NEC Laboratories America Andrew Ng Computer Science Dept. Stanford University
18. The image classification setting for analysis Implication : Learning an image classifier is a matter of learning nonlinear functions on patches. Sparse Coding Dense local feature Linear Pooling Linear SVM Function on images Function on patches
19. Illustration: nonlinear l earning via local coding 05/13/11 data points bases locally linear
20. How to learn a nonlinear function? 05/13/11 S tep 1: Learning the dictionary from unlabeled data
21. How to learn a nonlinear function? 05/13/11 S tep 2: Use t he dictionary to encode data
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23. L ocal Coordinate Coding (LCC): connect coding to n onlinear f unction l earning 05/13/11 Locality term Function approximation error Coding error If f(x) is (alpha, beta)-Lipschitz smooth Yu et al NIPS-09 T he key message: A good coding scheme should 1. have a small coding error, 2. and also b e sufficiently local
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27. The larger dictionary, the higher accuracy, but also the higher computation cost 05/13/11 T he same observation for Caltech-256, PASCAL, ImageNet, … Yu et al NIPS-09 Y ang et al CVPR 09
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29. C ompetitive in accuracy, cheap in computation 05/13/11 Wang et al CVPR 10 Sparse coding Significantly better than sparse coding T his is one of the two major algorithms applied by NEC-UIUC team to achieve the No.1 position in ImageNet challenge 2010! Comparable with sparse coding
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31. Interpret “BoW + linear classifier” data points cluster centers Piece-wise local constant ( zero-order)
32. Super-vector coding: a simple geometric way to improve BoW (VQ) Zhou et al, ECCV 10 data points cluster centers Piecewise local linear ( first-order) Local tangent
33. Super-vector coding: a simple geometric way to improve BoW (VQ) 05/13/11 Q uantization error Function approximation error If f(x) is beta-Lipschitz smooth, and Local tangent
34. Super-vector coding: learning nonlinear function via a global linear model 05/13/11 Let be the VQ coding of T his is one of the two major algorithms applied by NEC-UIUC team to achieve the No.1 position in PASCAL VOC 2009! Global linear weights to be learned S uper-vector codes of data
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40. Learning hierarchical dictionary 05/13/11 Jenatton, Mairal, Obozinski, and Bach, 2010 A node can be active only if its ancestors are active.
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Notas do Editor
Let’s further check what’s happening when best classification performance is achieved.