Texture pattern classification based on probability density function estimation of the image spatial structure feature with symmetrical weibull distribution model

Abstract

Automatic texture pattern classification (ATPC) has long been an essential issue in computer vision. However, ATPC is still a challenging task since texture is a subjective conception, which is difficult to be expressed concisely by the existing computational models. The visual appearance of the imaged texture pattern (TP) visually depends on the random organization of local homogeneous fragments (LHFs) in it. Hence, it is essential to investigate the latent statistical distribution (LSD) behavior of LHFs for the texture image spatial structural feature (TISSF) characterization and expression to achieve a good performance of ATPC. We presented a probability density function estimation (PDFE)-based ATPC scheme, termed PDFE-ATPC. We demonstrated the Weibull distribution (WD) behavior of LHFs by the sequential fragmentation theory to explain the LSD of the TISSFs. To obtain the multiscale and multi-orientation detail expression of the TISSFs, we introduced an oriented Gaussian derivative filter (OGDF)-based TISSF characterization method, including the steerable isotropic Gaussian derivative filters (SIGDFs) and the oriented anisotropic Gaussian Derivative filters (OAGDFs). Successively, the LSDs of the filter responses were characterized omnidirectionally by a symmetrical WD model (SWDM) and the SWDM-based TISSF parameters, demonstrated to be directly related to the human vision perception (HVP) system, were extracted and applied to the ATPC with a spline regression-based classifier. Effectiveness of the proposed PDFE-ATPC method was verified by extensive experiments on three different texture image databases and compared with four commonly-used statistics-based texture classification methods.