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ACM OpenSource Award 2010

The VLFeat open source library implements popular computer vision algorithms including SIFT, MSER, k-means, hierarchical k-means, agglomerative information bottleneck, and quick shift. 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;.

Documentation

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;
24/12/2011 VLFeat 0.9.14 released
VLFeat 0.9.14 adds SLIC superpixels, improves the documentation, and contains other improvements and bugfixes. Furthermore, starting from this release VLFeat is distributed under the BSD license. [Details].
12/7/2011 VLFeat 0.9.13 released
VLFeat 0.9.13 fixes the Windows binary package. [Details].
5/7/2011 VLFeat 0.9.12 released
VLFeat 0.9.12 contains minor bugfixes. [Details].
19/6/2011 VLFeat 0.9.11 released
VLFeat 0.9.11 solves a compatibility issue with old versions of Mac OS X and brings other minor bug fixes as well. [Details].
11/6/2011 VLFeat 0.9.10 released
VLFeat 0.9.10 rolls out numerous bug fixes and improvements, especially to the homogeneous kernel map implementation. [Details].
28/10/2010 VLFeat wins the ACM Multimedia Open Source Awards!
VLFeat 0.9.9 was awarded the ACM Multimedia Open Source Award 2010.
14/6/2010 VLFeat 0.9.9 released
VLFeat 0.9.9 adds a new sample application (SIFT matching) and minor refinements. [Details].
14/6/2010 Open Source Vision Software Tutorial
VLFeat presented at the CVPR 2010 Open Source Vision Software Tutorial. Slides of the presentation are available from the tutorial web page.
10/5/2010 VLFeat 0.9.8 released
VLFeat 0.9.8 adds new tutorials, (hierarchical) k-means support for floating point data, homogeneous kernel maps, a basic implementation of PEGASOS for SVM learning, and many other improvements. [Details].