Multi-ring local binary patterns for rotation invariant texture classification

Abstract

The local binary pattern (LBP) approach has been widely used in texture description. In this paper, we build a new framework to extract the binary patterns and propose a robust texture descriptor: multi-ring local binary pattern (MrLBP). The MrLBP algorithm creates patterns from several ringed areas and mainly contains two parts. One is the extra-ring local binary pattern operator that gets patterns from the mean values of different ringed areas. The other is the intra-ring local binary pattern operator that obtains patterns by counting the majority of binary values in every single ringed area. Moreover, the binary formation of each part of the MrLBP is obtained from two different aspects. The MrLBP method not only considers the binary relationship among pixels in a local region, but also focuses on the relationship between pixels in a local region and the whole image. This is a little different from the conventional LBP methods that only get the binary formation from the local gray scales differences. The experimental results on two public databases have validated the effectiveness of the proposed method.