Open AccessEfficient image classification via sparse coding spatial pyramid matching representation of SIFT‐WCS‐LTP feature
Author(s) -
Huang Mingming,
Mu Zhichun,
Zeng Hui
Publication year - 2016
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.2015.0329
Subject(s) - scale invariant feature transform , pattern recognition (psychology) , artificial intelligence , pyramid (geometry) , computer science , feature extraction , neural coding , feature (linguistics) , computer vision , mathematics , linguistics , philosophy , geometry
Shape and texture information are critical to the accuracy of image classification systems. In this study, the authors propose a novel descriptor called weighted centre‐symmetric local ternary pattern (WCS‐LTP), better characterising the image local texture. Then, based on the proposed WCS‐LTP descriptor, they introduce a new local scale invariant feature transform and WCS‐LTP (SIFT–WCS‐LTP) feature extraction approach. Compared with conventional local CS‐LTP and SIFT features, the authors’ proposed SIFT–WCS‐LTP feature can not only capture the shape information of images, but also tend to extract more precise texture information. Finally, SIFT–WCS‐LTP feature‐based sparse coding spatial pyramid matching (ScSPM) representation classification is proposed for image classification. Extensive experimental results demonstrate that the effectiveness of their proposed SIFT–WCS‐LTP feature‐based ScSPM representation classification algorithm.