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 compo- nents 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-the- art texture representation methods, especially in small training samples condition.
Highlights
Texture analysis is widely recognized as a difficult and challenging computer vision problem
We proposed an efficiency application of saddle points added Bidimensional Empirical Mode Decomposition (EMD) (BEMD) [32] combined with Local Binary Pattern (LBP) in texture www.ijacsa.thesai.org (IJACSA) International Journal of Advanced Computer Science and Applications, Vol 4, No 9, 2013 classification, and present the effectiveness of BEMD/Bidimensional Intrinsic Mode Functions (BIMFs) invariant properties for texture images
We further extend the LBP-BEMD feature to variance normalization and BIMFs selection for performance improvement
Summary
Texture analysis is widely recognized as a difficult and challenging computer vision problem. Basic Image Features presented by Griffin and Lillholm [2] are defined by a partition of the filter-response space of a set of six Gaussian derivative filters and the set of filters describes an image locally up to second order at some scale Those methods are all state-of-the-art statistics algorithm and present good classification results on many databases. Local Binary Pattern (LBP) is used as texture descriptor to detect the features of texture images’ BIMFs. BEMD decomposed the original image to new multi-scale components (Bidimensional Intrinsic Mode Functions). BEMD decomposed the original image to new multi-scale components (Bidimensional Intrinsic Mode Functions) In those new components, LBP histograms can achieve better efficiency than in the original image and present more illumination invariant features to supplement LBP to improve classification accuracy while preserving its simplicity. We provide a more in-depth analysis and more extensive evaluation
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
