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ACM OpenSource Award 2010

The VLFeat open source library implements popular computer vision algorithms including HOG, SIFT, MSER, k-means, hierarchical k-means, agglomerative information bottleneck, SLIC superpixels, 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;`.

# Download

- VLFeat %env:VERSION; (Windows, Mac, Linux)
- Source code and installation
- repository, bug tracking.

# Documentation

- MATLAB commands
- C API with algorithm descriptions
- Command line tools

# Tutorials

- Features: Covariant detectors, HOG, SIFT, MSER, Quick shift, SLIC
- Clustering: IKM, HIKM, AIB
- Matching: Randomized kd-trees
- All tutorials

# Example applications

- Caltech-101 classification
- SIFT matching for auto-stitching
- All example 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;

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, estiamtion of feature orientation, computation of descriptors on the affine patches (including raw patches), and sourcing of custom feature frame. Addet 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.