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https://github.com/vlfeat/vlfeat

An open library of computer vision algorithms
https://github.com/vlfeat/vlfeat

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An open library of computer vision algorithms

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README 

        
# VLFeat -- Vision Lab Features Library



> Version 0.9.21



The VLFeat open source library implements popular computer vision

algorithms specialising in image understanding and local featurexs

extraction and matching.  Algorithms incldue Fisher Vector, VLAD,

SIFT, MSER, k-means, hierarchical k-means, agglomerative information

bottleneck, SLIC superpixes, 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.



VLFeat is distributed under the BSD license (see the `COPYING` file).



The documentation is

[available online](http://www.vlfeat.org/index.html) and shipped with

the library as `doc/index.html`. See also:



* [Using with MATLAB](http://www.vlfeat.org/install-matlab.html)

* [Using the command line utilities](http://www.vlfeat.org/install-shell.html)

* [Using the C API](http://www.vlfeat.org/install-c.html)

* [Compiling from source](http://www.vlfeat.org/compiling.html)



## Quick start with MATLAB



To start using VLFeat as a MATLAB toolbox, download the latest VLFeat

[binary package](http://www.vlfeat.org/download/). Note that the

pre-compiled binaries require MATLAB 2009B and later. Unpack it, for

example by using WinZIP (Windows), by double clicking on the archive

(Mac), or by using the command line (Linux and Mac):



    > tar xzf vlfeat-X.Y.Z-bin.tar.gz



Here X.Y.Z denotes the latest version. Start MATLAB and run the

VLFeat setup command:



    > run /toolbox/vl_setup



Here `` should be replaced with the path to the VLFeat

directory created by unpacking the archive. All VLFeat demos can now

be run in a row by the command:



    > vl_demo



Check out the individual demos by editing this file: `edit vl_demo`.



## Octave support



The toolbox should be laregly compatible with GNU Octave, an open

source MATLAB equivalent. However, the binary distribution does not

ship with pre-built GNU Octave MEX files. To compile them use



    > cd 

    > make MKOCTFILE=



# Changes



- **0.9.21** Maintenance release. Bugfixes.

- **0.9.20** Maintenance release. Bugfixes.

- **0.9.19** Maintenance release. Minor bugfixes and fixes compilation

  with MATLAB 2014a.

- **0.9.18** Several bugfixes. Improved documentation, particularly of

  the covariant detectors. Minor enhancements of the Fisher vectors.

- **0.9.17** Rewritten SVM implementation, adding support for SGD and

  SDCA optimisers and various loss functions (hinge, squared hinge,

  logistic, etc.) and improving the interface. Added infrastructure to

  support multi-core computations using OpenMP (MATLAB 2009B or later

  required). 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.

- **0.9.16** Added `VL_COVDET`. This function implements the following

  detectors: DoG, Hessian, Harris Laplace, Hessian Laplace, Multiscale

  Hessian, Multiscale Harris. It also implements affine adaptation,

  estiamtion of feature orientation, computation of descriptors on the

  affine patches (including raw patches), and sourcing of custom

  feature frame.

- **0.9.15** Added `VL_HOG` (HOG features). Added `VL_SVMPEGASOS` and

  a vastly improved SVM implementation. Added `VL_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`.

- **0.9.14** Added SLIC superpixels. Added VL_ALPHANUM(). Improved

  Windows binary package and added support for Visual

  Studio 2010. Improved the documentation layout and added a proper

  bibliography. Bugfixes and other minor improvements. Moved from the

  GPL to the less restrictive BSD license.

- **0.9.13** Fixed Windows binary package.

- **0.9.12** Fixes `vl_compile` and the architecture string on Linux 32 bit.

- **0.9.11** Fixes a compatibility problem on older Mac OS X versions.

  A few bugfixes are included too.

- **0.9.10** Improves the homogeneous kernel map. Plenty of small

  tweaks and improvements. Make maci64 the default architecture on the

  Mac.

- **0.9.9** Added: sift matching example. Extended Caltech-101

  classification example to use kd-trees.

- **0.9.8** Added: image distance transform, PEGASOS, floating point

  K-means, homogeneous kernel maps, a Caltech-101 classification

  example. Improved documentation.

- **0.9.7** Changed the Mac OS X binary distribution to require a less

  recent version of Mac OS X (10.5).

- **0.9.6** Changed the GNU/Linux binary distribution to require a

  less recent version of the C library.

- **0.9.5** Added kd-tree and new SSE-accelerated vector/histogram

  comparison code.  Improved dense SIFT (dsift) implementation.  Added

  Snow Leopard and MATLAB R2009b support.

- **0.9.4** Added quick shift. Renamed dhog to dsift and improved

  implementation and documentation. Improved tutorials.  Added 64 bit

  Windows binaries. Many other small changes.

- **0.9.3** Namespace change (everything begins with a vl_ prefix

  now). Many other changes to provide compilation support on Windows

  with MATLAB 7.

- **beta-3** Completes to the ikmeans code.

- **beta-2** Many additions.

- **beta-1** Initial public release.