Article,
Texture Classification based on Bidimensional Empirical Mode Decomposition and Local Binary Pattern
International Journal of Advanced Computer Science and Applications(IJACSA), (2013)
Abstract
This paper presents a new simple and robust
texture analysis feature based on Bidimensional Empirical Mode
Decomposition (BEMD) and Local Binary Pattern (LBP). BEMD
is a locally adaptive decomposition method and suitable for the
analysis of nonlinear or nonstationary signals. Texture images are
decomposed to several Bidimensional Intrinsic Mode Functions
(BIMFs) by BEMD, which present a new set multi-scale components
of images. In our approach, firstly, saddle points are added
as supporting points for interpolation to improve original BEMD,
and then images are decomposed by the new BEMD to several
components (BIMFs). After then, Local Binary Pattern (LBP) in
different sizes is used to detect features from different BIMFs.
At last, normalization and BIMFs selection method are adopted
for features selection. The proposed feature presents invariant
while preserving LBP’s simplicity. Our method has also been
evaluated in CuRet and KTH-TIPS2a texture image databases.
It is experimentally demonstrated that the proposed feature
achieves higher classification accuracy than other state-of-theart
texture representation methods, especially in small training
samples condition.
