FVKit: Matlab code to extract Fisher Vectors
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Fisher Vector Extraction Kit provides a Matlab implementation to extract Fisher Vectors from images

This FVKit package provides a Matlab implementation to extract Fisher Vectors from images/videos. It is written with a two fold reasoning: First to provide an easy to understand extraction pipeline for visual features. Second, for extensive experiments with FV. (This second motivation is somewhat outdated with the raise of ConvNets).

The FVKit is written completely in Matlab, and only have dependencies for SVM classification and for extraction the local SIFT features (see below).

The FVKit has been used to extract the features for the Rijksmuseum Challnge Github, introduced in [mensink14icmr].

Demo and Comparison on Pascal VOC 2007

The package contains a demo script (demo_voc2007) to show the usage and to run some experiments on the Pascal VOC 2007 dataset.

Download Pascal VOC 2007

Download Pascal VOC2007 dataset to the data/voc2007 directory.

  • See download (and extraction) script in data directory
  • The IMDB file -which provides the ground truth and training and testing splits- provided in the data/voc2007 directory is obtained from:

Below the results are shown for some different parameters. As baseline system we follow largely the paper of [sanchez13ijcv]:

  • Extract SIFT from grey valued images
  • PCA to 60 dimensions + 4 dimensions for location, SIFT norm and scale
  • Gaussian Mixture Model with 256 components
  • FV with closed form approximation of FIM, and derivatives with respect to mean and variance
  • Pooling: extract one FV per image
  • LibSVM with linear kernel, cross-validate value of C (single C value for all classes)

Experiments with PCA, Color SIFT and Pooling

PCA dimension mAP Color SIFT mAP Pooling mAP
32 (28+4) 57.70 Intensity 61.23 Full 61.23
64 (60+4) 61.23 Opponent 58.16 F+Horiz 61.71
128 (124+4) 62.29 Hue 56.61 FH+Quad 62.23

Experiments with number of GMM components

GMM Components 16 32 64 128 256
mAP 53.12 56.19 58.12 59.63 61.23

The settings to run these experiments is available in demo_voc2007.m

Comparison to other implementations We compare to two other papers, including results on Pascal VOC 2007 and indicate some of the key differences

  • [sanchez13ijcv] they do not provide code, but one of the core reasons for the initial development of the FVKit package was to reprocude their results
  • [chatfield11bmvc] they do provide code, for many more (old-fashioned) visual encodings, yet especially the FV code is not very clear/intuitive to use nor to extend for other research in Fisher Vectors.
Paper / Package mAP
[sanchez13ijcv] 61.8
[chatfield11bmvc] 61.7
FVKit Matlab 62.2

Results differ, among others, due to:

  • different PCA dimensions (64 vs 80);
  • different SIFT extraction code (proprietary vs VL-Feat);
  • use of LNS encoding and square rooting (this package);
  • differences due to implementation (eg EM, SVM, etc)

Demo and Comparison on Rijksmusem Challenge 2014

The package contains a demo script (demo_RMC) to extract FV for the RMC challenge and evaluate the performance on the Creator challenge

For more information about the Rijksmusem Challenge:

The demo script extract FV and then run the Creator challenge. It obtains the following results

Creator Challenge MCA: all 374 300 200 100
FV Kit demo 55.6 69.3 71.5 75.4 79.8
[mensink14icmr] 51.0 65.5 67.6 71.2 75.8

These differences are (likely) due to square rooting of SIFT features and the LNS encoding of projected features. The features used in [mensink14icmr] are available for download (see above)



When using this code, please cite the following papers

  author = {Thomas Mensink and Jan van Gemert},
  title = {The Rijksmuseum Challenge: Museum-Centered Visual Recognition},
  booktitle = {ACM International Conference on Multimedia Retrieval (ICMR)},
  year = {2014}

   author = {Jorge Sánchez and Florent Perronnin and Thomas Mensink and Jakob Verbeek},
   title = {Image Classification with the Fisher Vector: Theory and Practice},
   journal = {International Journal on Computer Vision (IJCV)},
   year = {2013},
  1. [chatfield11bmvc] Chatfield et al., The devil is in the details: an evaluation of recent feature encoding methods, BMVC 2011
  2. [mensink14icmr] Mensink and van Gemert, The Rijksmuseum Challenge: Museum-Centered Visual Recognition, ICMLR 2014 (pdf)
  3. [sanchez13ijcv] Sanchez et al., The Fisher Vector: Theory and Practice, IJCV 2014 (pdf)

Copyright (2013-2018)

Thomas Mensink, University of Amsterdam, thomas.mensink@uva.nl: