Study of statistical methods for texture analysis and their modern evolutions

Abstract

Texture analysis is widely performed in the current time as it is considered as an intimate property of the surface. It is widely used in the field of image processing, remote sensing applications, biomedical analysis, document processing, and so on. In this investigation, we present a detailed study of four different methodologies that have been developed for texture classification. These methodologies include gray level cooccurrence matrix (GLCM), local binary pattern (LBP), autocorrelation function (ACF), and histogram pattern. The detailed investigation on these methods suggests that GLCM is best for analyzing the surface texture, land-use/landcover classification, and satellite data processing. LBP is widely used to analyze the facial features of an individual. The autocorrelation is used to identify the regularity of the textured surface. Finally, through histograms, one can visually identify the changes developed while analyzing the texture of the image data. Furthermore, we present a brief summary for newly developed texture classification techniques such as binary Gabor pattern, local spiking pattern, SRITCSD method, scale inversion, and deep perception models for texture analysis. Some benchmark texture datasets used in image processing are also discussed in this work. K E Y W O R D Sautocorrelation function, gray level cooccurrence matrix, histogram, local binary pattern, texture 1 This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Here, we have plotted the histogram signature plot for the original and encrypted images. The histogram is a computer‐generated approximate representation of numerical data 48 . The histogram of the original image and its encrypted version show similarity with each other.…”

“…The histogram is a computer-generated approximate representation of numerical data. 48 The histogram of the original image and its encrypted version show similarity with each other. The images formed by using only 10% of pixels show a similar histogram as compared with its original image.…”

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