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 [1] the number of support regions
-lsRadius [6] the local sampling radius of each pixel
-normPatchWidth [41] the size of the normalized patch
-liopType [1] weight type of LIOP, 1 for uniform weighing used in PAMI paper, 2 for weighting used in ICCV paper
-liopRegionNum [6] the number of ordinal bins in LIOP
-liopNum [4] the number of local sampling points around each pixel in LIOP
-oiopType [1] the quantization strategy of OIOP, 1 for learning based quantization, 2 for standard quantization
-oiopRegionNum [4] the number of ordinal bins in OIOP
-oiopNum [3] the number of local sampling points around each pixel in OIOP
-oiopQuantLevel [4] the number of quantization levels in OIOP
-pcaFile the PCA parameters for MIOP
-pcaBasisNum [128] the expected dimension after PCA in MIOP
-isApplyPCA [0] applying PCA dimension reduction or not
1.0
OpenCV 3.0+
[1] Zhenhua Wang, Bin Fan and Fuchao Wu, “Local Intensity Order Pattern for Feature Description", IEEE International Conference on Computer Vision (ICCV) , Nov. 2011
[2] 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.