Open AccessMulti‐scale microstructure binary pattern extraction and learning for image representation
Author(s) -
Zhang Dongbo,
Yi Lianglin,
Tang Hongzhong,
Zhang Ying,
Xu Haixia
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.6358
Subject(s) - pattern recognition (psychology) , robustness (evolution) , artificial intelligence , binary number , feature extraction , computer science , binary image , local binary patterns , coding (social sciences) , representation (politics) , image (mathematics) , image processing , mathematics , histogram , biochemistry , chemistry , statistics , arithmetic , politics , political science , law , gene
In this study, an image representation method based on multi‐scale microstructural binary pattern extraction is proposed, which uses zero‐mean microstructural pattern binarisation. This method can express all kinds of important pattern structures that may appear in the image. By using the dominant binary pattern learning model, the dominant feature pattern sets adapted to different datasets can be obtained, which have good performance in the aspects of feature robustness, recognition, and representation ability. This method can greatly reduce the dimension of feature coding and improve the speed of the algorithm. The experimental results show that this method has strong recognition ability and robustness, is superior to the traditional local binary pattern and grey image micorstructure maximum response pattern methods, and has a competitive performance compared with the results of many latest algorithms.