A fractal-based BEMD method for image texture analysis

Abstract

This paper presents a new method for texture analysis through Bidimensional Empirical Mode Decomposition (BEMD). Although there have been many filter based methods for texture analysis, problems of non-adaptively and redundancy are still hard to solve. The BEMD is a locally adaptive method and suitable for the analysis of nonlinear or nonstationary signals. The texture image can be decomposed to several IMFs (intrinsic mode functions) by BEMD, which present new characters of the images. But for the BEMD, the boundary interference is a main limit for its application. In this paper, we proposed a new BEMD method based on the self-similar extend method and the neighbor local extremes to reduce the boundary interference. This new method can get a lower orthogonality index (OI) of the IMF, which present more clearly features of the texture images. The experiment result shown the new method also reduced the computation complex compared to other surface interpolation based methods.