Intensity Order based Local Features
IntensityOrderFeature is open source with a public repository on GitHub. It includes the LIOP, OIOP and MIOP descriptors that are published in [1,2].
This is a free software. You can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. You should have received a copy of the GNU General Public License along with this software. If not, see http://www.gnu.org/licenses/.
Command line arguments:
-img the input image file -kp the input region file -des the output descriptor file -type [liop] liop/oiop/miop/miop_fast -initSigma [0.0] Gaussian sigma for pre-smoothing -nSigma [1.2] Gaussian sigma for smoothing after normalization -srNum  the number of support regions -lsRadius  the local sampling radius of each pixel -normPatchWidth  the size of the normalized patch -liopType  weight type of LIOP, 1 for uniform weighing used in PAMI paper, 2 for weighting used in ICCV paper -liopRegionNum  the number of ordinal bins in LIOP -liopNum  the number of local sampling points around each pixel in LIOP -oiopType  the quantization strategy of OIOP, 1 for learning based quantization, 2 for standard quantization -oiopRegionNum  the number of ordinal bins in OIOP -oiopNum  the number of local sampling points around each pixel in OIOP -oiopQuantLevel  the number of quantization levels in OIOP -pcaFile the PCA parameters for MIOP -pcaBasisNum  the expected dimension after PCA in MIOP -isApplyPCA  applying PCA dimension reduction or not
 Zhenhua Wang, Bin Fan and Fuchao Wu, “Local Intensity Order Pattern for Feature Description", IEEE International Conference on Computer Vision (ICCV) , Nov. 2011
 Zhenhua Wang, Bin Fan, Gang Wang and Fuchao Wu, “Exploring Local and Overall Ordinal Information for Robust Feature Description", IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2016.