Abstract :
The aim of this study was to investigate the statistical meaning of six GLCM (Gray Level Cooccurrence Matrix) parameters. This objective was mainly pursued by means of a self-consistent, theoretical assessment in order to remain independent from test image. The six statistical parameters are energy, contrast, variance, correlation, entropy and inverse difference moment, which are considered the most relevant among the 14 originally proposed by Haralick et al. The functional analysis supporting theoretical considerations was based on natural clustering in the feature space of segment texture values. The results show that among the six GLCM statistical parameters, five different sets can be identified, each set featuring a specific textural meaning. The first set contains energy and entropy, while the four remaining parameters can be regarded as belonging to four different sets. Two parameters, energy and contrast, are considered to be the most efficient for discriminating different textural patterns. A new GLCM statistical parameter, recursivity, is presented in order to replace energy which presents some degree of correlation with contrast. It is demonstrated that in some cases it may be reasonable to replace the computation of GLCM with that of GLDH (Gray Level Difference Histogram), in order to benefit by a better compromise between texture measurement accuracy, computer storage and computation time
