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The VLFeat open source
library implements popular computer vision algorithms specializing
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. The latest version of VLFeat
is %env:VERSION;
.
Download
|
Documentation
|
Tutorials
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;
- 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, 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.