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  The VLFeat open source
  library implements popular computer vision algorithms specializing
  in image understanding and local features extraction and
  matching. Algorithms include Fisher Vector, VLAD, SIFT, MSER,
  k-means, hierarchical k-means, agglomerative information bottleneck,
  SLIC superpixels, quick shift superpixels, large scale SVM training,
  and many others. It is written in C for efficiency and
  compatibility, with interfaces in MATLAB for ease of use, and
  detailed documentation throughout. It supports Windows, Mac OS X,
  and Linux. The latest version of VLFeat
  is %env:VERSION;.
 
 
   | Download | Documentation | 
 
   | TutorialsExample applications | Citing
@misc{vedaldi08vlfeat,
 Author = {A. Vedaldi and B. Fulkerson},
 Title = {{VLFeat}: An Open and Portable Library
          of Computer Vision Algorithms},
 Year  = {2008},
 Howpublished = {\url{http://www.vlfeat.org/}}
Acknowledgments
          UCLA Vision
         Lab
       Oxford
         VGG. | 
 
 News
 &nsbp;
 
   - 14/1/2015 VLFeat 0.9.20 released
- Maintenance release. Bugfixes.
- 12/9/1014
   MatConvNet
- Looking for an easy-to-use package to work with deep
   convolutional neural networks in MATLAB? Check out our
   new MatConvNet
   toolbox!
- 12/9/2014 VLFeat 0.9.19 released
- Maintenance release. Minor bugfixes and fixes compilation with
   MATLAB 2014a.
- 29/01/2014 VLFeat 0.9.18 released
- Several bugfixes. Improved documentation, particularly of the
   covariant detectors. Minor enhancements of the Fisher vectors.
   [Details]
   
- 22/06/2013 VLFeat 0.9.17 released
- Rewritten SVM implementation, adding support for SGD and SDCA
   optimizers and various loss functions (hinge, squared hinge,
   logistic, etc.) and improving the interface. Added infrastructure
   to support multi-core computations using OpenMP. Added OpenMP
   support to KD-trees and KMeans. Added new Gaussian Mixture Models,
   VLAD encoding, and Fisher Vector encodings (also with OpenMP
   support). Added LIOP feature descriptors. Added new object category
   recognition example code, supporting several standard benchmarks
   off-the-shelf. This is the third point update supported by
   the PASCAL Harvest
   programme.
   [Details]
   
- 01/10/2012
     VLBenchmarks
       1.0-beta released.
- This new project provides simple to use benchmarking code for
   feature detectors and descriptors. Its development was supported by
   the PASCAL Harvest
   programme.
   [Details]
- 01/10/2012 VLFeat 0.9.16 released
- Added VL_COVDET() (covariant feature detector). This function
   implements the following detectors: DoG, Hessian, Harris Laplace,
   Hessian Laplace, Multiscale Hessian, Multiscale Harris. It also
   implements affine adaptation, estimation of feature orientation,
   computation of descriptors on the affine patches (including raw
   patches), and sourcing of custom feature frame. Added the auxiliary
   function VL_PLOTSS(). This is the second point update supported by
   the PASCAL Harvest
   programme.
   [Details]
- 11/9/2012 VLFeat 0.9.15 released
- Added VL_HOG() (HOG features). Added VL_SVMPEGASOS() and a
   vastly improved SVM implementation. Added IHASHSUM (hashed
   counting). Improved INTHIST (integral histogram). Added
   VL_CUMMAX(). Improved the implementation of VL_ROC() and
   VL_PR(). Added VL_DET() (Detection Error Trade-off (DET)
   curves). Improved the verbosity control to AIB. Added support for
   Xcode 4.3, improved support for past and future Xcode
   versions. Completed the migration of the old test code in
   toolbox/test, moving the functionality to the new unit tests
   toolbox/xtest. Improved credits. This is the first point update
   supported by the PASCAL
   Harvest (several more to come shortly). A big thank to our
   sponsor!
   [Details].
   
- 10/1/2012
     PASCAL2 Harvest funding
- In the upcoming months many new functionalities will be added
     to VLFeat thanks to the PASCAL
     Harvest! See here for details.