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Intensity Order based Local Features (LIOP, OIOP, MIOP)
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README.md

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/.

Usage

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

Version

1.0

Requirement

OpenCV 3.0+

Reference:

[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.

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