You will need:
http://lear.inrialpes.fr/src/inria_fisher/inria_fisher_v1.tgz tar xvzf inria_fisher_v1.tgz cd inria_fisher_v1 wget http://lear.inrialpes.fr/src/inria_fisher/inria_fisher_data_v1.tgz tar xvzf inria_fisher_data_v1.tgz wget http://pascal.inrialpes.fr/data/holidays/siftgeo.tar.gz tar xzf siftgeo.tar.gz mkdir -p yael/matlab cd yael/matlab wget https://gforge.inria.fr/frs/download.php/30399/yael_matlab_linux64_v277.tar.gz tar xvzf yael_matlab_linux64_v277.tar.gz cd ../..Then run
test_fisher
in Matlab. This computes Fisher descriptors (k=64) for the Holidays dataset
(from the local descriptors in siftgeo/). Then it performs exact NN-searches with the L2 distance
on the Holidays query images.
After about 10 minutes, it should display:
Fisher k=64 4096D mAP = 0.599 Fisher + PCA (D'=128) 128D mAP = 0.561These numbers correspond to the 59.5 and 56.5 figures in Table I of the PAMI paper (the implementation is not exactly the same).
The PQ compression is not implemented in this version. GMM learning is implemented in Yael but not interfaced in Matlab. Dense descriptors local are implemented in compute_descriptors (-dense
option) but no corresponding GMM is provided yet.
You may be interested in this package, that also implements compression with PQ codes (on different descriptors).
@article{JEGOU-2011-633013, url = {http://hal.inria.fr/inria-00633013}, title = {{Aggregating local image descriptors into compact codes}}, author = {J{\'e}gou, Herv{\'e} and Perronnin, Florent and Douze, Matthijs and S{\'a}nchez, Jorge and P{\'e}rez, Patrick and Schmid, Cordelia}, publisher = {IEEE}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, year = {2011}, pdf = {http://hal.inria.fr/inria-00633013/PDF/jegou\_aggregate.pdf}, }
matthijs dot douze at inria dot fr.It is licensed under Cecill, similar to the GPL. Please note that some techniques (eg. SIFT and Fisher) may be covered by non-INRIA software patents.
Last modification on 2012-04-09 by Matthijs Douze.