Курсы английского
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Fisher vector idea
Fisher vector idea
Fisher vector for image classification
Fisher vector for image classification
Fisher vector for image classification
Fisher vector for image classification
Fisher vector for image classification
Fisher vector for image classification
Fisher vector for image classification
Fisher vector for image classification
Fisher vector for image classification
Fisher vector for image classification
Whitening the data
Whitening the data
Whitening the data
Whitening the data
Whitening the data
Whitening the data
Classification with Fisher kernels
Classification with Fisher kernels
Improvements to Fisher Kernels
Improvements to Fisher Kernels
Improvements to Fisher Kernels
Improvements to Fisher Kernels
Improvements to Fisher Kernels
Improvements to Fisher Kernels
Improvements to Fisher Kernels
Improvements to Fisher Kernels
Improvement: power normalization
Improvement: power normalization
Improvement: power normalization
Improvement: power normalization
Results: Pascal 2007
Results: Pascal 2007
Results: Pascal 2007
Results: Pascal 2007
Results: Caltech 256
Results: Caltech 256
PASCAL + additional training data
PASCAL + additional training data
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Improving the Fisher Kernel for Large-Scale Image Classi?cation

содержание презентации «Improving the Fisher Kernel for Large-Scale Image Classi?cation.ppt»
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1Improving the Fisher Kernel for 8and Dance//CVPR07] suggest a diagonal
Large-Scale Image Classi?cation. Florent approximation to Fisher matrix:
Perronnin, Jorge Sanchez, and Thomas 9Classification with Fisher kernels.
Mensink, ECCV 2010. VGG reading group, Use whitened Fisher vectors as an input to
January 2011, presented by V. Lempitsky. e.g. linear SVM Small codebooks (e.g. 100
2From generative modeling to features. words) are sufficient Encoding runs faster
dataset. Discriminative classfier model. than BoW with large codebooks (although
Input sample. Generative model. fitting. with approximate NN this is not so
Parameters of the fit. straightforward!) Slightly better accuracy
3Simplest example. Dataset of vectors. than “plain, linear BoW”. F. Peronnin and
Discriminative classfier model. Input C. Dance // CVPR 2007.
vector. K-means. Codebook. fitting. 10Improvements to Fisher Kernels. =0.
Codebooks Sparse or dense component Perronnin, Jorge Sanchez, and Thomas
analysis Deep belief networks Color GMMs Mensink, ECCV 2010. Overall very similar
.... Closest codeword. to how people improve regular BoW
4Fisher vector idea. Jaakkola, T., classification. Idea 1: normalization of
Haussler, D.: Exploiting generative models Fisher vectors. Justification: our GMM.
in discriminative classi?ers. NIPS’99. probability distribution of VW in an
Generative model. Discriminative classfier image. Assume: Image specific “content”.
model. Input sample. Parameters of the Then: Thus: Observation: image
fit. fitting. Information loss (generative non-specific “content” affects the length
models are always inaccurate!). Can we of the vector, but not direction.
retain some of the lost information Conclusion: normalize to remove the effect
without building better generative model? of non-specific “content” ...also
Main idea: retain information about the L2-normalization ensures K(x,x) = 1 and
fitting error for the best fit. Same best improves BoV [Vedaldi et al. ICCV’09].
fit, but different fitting errors! 11Improvement: power normalization. ?
5Fisher vector idea. X. ? (?1,?2). =0.5 i.e. square root works well c.f. for
Fisher vector: Jaakkola, T., Haussler, D.: example [Vedaldi and Zisserman// CVPR10]
Exploiting generative models in or [Peronnin et al.//CVPR10] on the use of
discriminative classi?ers. NIPS’99. square root and Hellinger’s kernel for
Generative model. Discriminative classfier BoW.
model. Input sample. Fisher vector. 12Improvement 3: spatial pyramids. Fully
fitting. Main idea: retain information standard spatial pyramids [Lazebnik et
about the fitting error of the best fit. al.] with sum-pooling.
6Fisher vector for image 13Results: Pascal 2007. Details: regular
classification. F. Peronnin and C. Dance grid, multiple scales, SIFT and local RGB
// CVPR 2007. Assuming independence color layout, both reduced to 64
between the observed T features Encoding dimensions via PCA.
each visual feature (e.g. SIFT) extracted 14Results: Caltech 256.
from image to a Fisher vector Using 15PASCAL + additional training data.
N-component gaussian mixture models with Flickr groups up to 25000 per class
diagonalized covariance matrices: N ImageNet up to 25000 per class.
dimensions. 128N dimensions. 128N 16Conclusion. Fisher kernels – good way
dimensions. to exploit your generative model Fisher
7Relation to BoW. BoW. Extra info. F. kernels based on GMMs in SIFT space lead
Peronnin and C. Dance // CVPR 2007. N to state-of-the-art results (on par with
dimensions. 128N dimensions. 128N the most recent BoW with soft assignments)
dimensions. Main advantage of FK over BoW are smaller
8Whitening the data. Fisher matrix dictionaries ...although FV are less
(covariance matrix for Fisher vectors): sparse than BoV Peronnin et al. trained
Whitening the data (setting the covariance their system within a day for 20 classes
to identity): Fisher matrix is hard to for 350K images on 1 CPU.
estimate. Approximations needed: [Peronnin
Improving the Fisher Kernel for Large-Scale Image Classi?cation.ppt
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Improving the Fisher Kernel for Large-Scale Image Classi?cation

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