VLBenchmark is a MATLAB suite of benchmarks for computer vision features
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Anders Boesen Lindbo Larsen
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+benchmarks HarrisScaleSelect and LindebergScaleSelect detectors updated. Jul 3, 2014
+datasets
+helpers Fixed problem caused by missing wget on some systems. Fixed paris dat… Oct 4, 2012
+localFeatures HarrisScaleSelect and LindebergScaleSelect detectors updated. Jul 3, 2014
doc doc/index.html: fixes link to package Oct 5, 2012
.gitignore .gitignore: doc byproducts Sep 20, 2012
.gitmodules adds webdoc submodule to generate the documentation Sep 8, 2012
LICENSE LICENSE: credits authors instead Sep 20, 2012
README README: credits Oct 5, 2012
benchmarkDemo.m Refactored testDetector to testFeatureExtractor as it is more precise. Sep 29, 2012
clean.m PCWIN fixes. Oct 3, 2012
eval_mh.m
install.m PCWIN fixes. Oct 3, 2012
repeatabilityDemo.m Program design changed to support datasets with different ground trut… Mar 7, 2013
reproduceIjcv05.m Refactored testDetector to testFeatureExtractor as it is more precise. Sep 29, 2012
retrievalDemo.m

README

                           VLBenchmakrs
                         Version 1.0-beta
              Karel Lenc   Varun Gulshan   Andrea Vedaldi


This package implements a collection of benchmkark routines to evaluate
local feature detectors and descriptors. See
http://www.vlfeat.org/benchmarks/index.html or the bundled copy
doc/index.html for installation and usage instructions.

CHANGES

v1.0-beta  (5-10-2012) First public release.

ACKNOWLEDGMENTS
    This project was supported by the PASCAL Harvest Programme 2012.

    Part of the package is based on code of Krystian Mikolajczyk
    as available here: http://www.robots.ox.ac.uk/~vgg/research/affine/.

    The authors would like to thank Andrew Zisserman, Jiri Matas,
    Krystian Mikolajczyk, Tinne Tuytelaars, and Cordelia Schmid for
    helpful discussion and supports.

REFERENCES

[1] K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman,
    J. Matas, F. Schaffalitzky, T. Kadir, and L. Van Gool. A
    comparison of affine region detectors. IJCV, 1(65):43–72, 2005.

[2] H. Jegou, M. Douze and C. Schmid,
    Exploiting descriptor distances for precise image search,
    Research report, INRIA 2011
    http://hal.inria.fr/inria-00602325/PDF/RA-7656.pdf