Exploring Local and Overall Ordinal Information for Robust Feature Description

IEEE Transactions on Pattern Analysis and Machine Intelligence

Zhenhua Wang, Bin Fan, Gang Wang and Fuchao Wu



This paper aims to build robust feature descriptors by exploring intensity order information in a patch. To this end, the Local Intensity Order Pattern~(LIOP) and the Overall Intensity Order Pattern~(OIOP) are proposed to effectively encode intensity order information of each pixel in different aspects. Specifically, LIOP captures the local ordinal information by using the intensity relationships among all the neighboring sampling points around a pixel, while OIOP exploits the coarsely quantized overall intensity order of these sampling points. These two kinds of patterns are then separately aggregated into different ordinal bins, leading to two kinds of feature descriptors. Furthermore, as these two kinds of descriptors could encode complementary ordinal information, they are combined together to obtain a discriminative and compact MIOP~(Mixed Intensity Order Pattern) descriptor. All these descriptors are constructed on the basis of relative relationships of intensities in a rotationally invariant way, making them be inherently invariant to image rotation and any monotonic intensity changes. Experimental results on image matching and object recognition are encouraging, demonstrating the superiorities of our descriptors over the state of the art.

Paper: pdf

Code: https://github.com/foelin/IntensityOrderFeature

If you have any questions about this paper, please contact the author (zhwang dot me [at] gmail dot com).