AbstractA recent tendency in the study of texture is the analysis based on mathematical models rather than the analysis of intuitively clear geometrical features. This type of study is advantageous in that a systematic and theoretical viewpoint can be provided to various kinds of related texture image processings. This paper considers the Gaussian‐Markov random field as a mathematical model for the texture image, and applications of the theory are discussed for texture classification and boundary extraction of texture regions. The determination of parameters for the Markov model is discussed first. Then the distance between textures is defined using the model parameters and a method of texture classification is proposed. The extraction of the boundary between texture regions is formulated as a testing of the parameters of the Markov model, and a method of texture region partition is proposed using the statistical testing variable as the discrimination function for the texture boundary. A classification experiment was performed for texture images with 13 categories, each containing 23 patterns. A 100% rate of classification was obtained, indicating the effectiveness of the proposed classification method. The region partition experiment was performed for the patched texture image. The result of partition was satisfactory, indicating the effectiveness of the proposed boundary extraction metho