Картинки на тему «Improving the Fisher Kernel for Large-Scale Image Classi?cation» |
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1 | Improving the Fisher Kernel for | 8 | and Dance//CVPR07] suggest a diagonal |
Large-Scale Image Classi?cation. Florent | approximation to Fisher matrix: | ||
Perronnin, Jorge Sanchez, and Thomas | 9 | Classification 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 | ||
2 | From 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 | ||
3 | Simplest example. Dataset of vectors. | than “plain, linear BoW”. F. Peronnin and | |
Discriminative classfier model. Input | C. Dance // CVPR 2007. | ||
vector. K-means. Codebook. fitting. | 10 | Improvements 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 | ||
4 | Fisher 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! | 11 | Improvement: power normalization. ? | |
5 | Fisher 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. | 12 | Improvement 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. | ||
6 | Fisher vector for image | 13 | Results: 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 | 14 | Results: Caltech 256. | |
from image to a Fisher vector Using | 15 | PASCAL + 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 | 16 | Conclusion. Fisher kernels – good way | |
dimensions. | to exploit your generative model Fisher | ||
7 | Relation 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 | ||
8 | Whitening 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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