Texture Features and Image Texture Models

Abstract

Image textureImage texture modelisModelimage texturean important phenomenon in many applications of pattern recognitionPattern recognitionand computer visionComputer vision. Hence, several models for deriving texture properties have been proposed and developed. Although there is no formal definition of image texture in the literature, image textureImage textureis usually considered the spatial arrangement of grayscale pixels in a neighborhood on the image. In this chapter, some widely used image texture methods for measuring and extracting texture features will be introduced. These textural features can then be used for image texture classificationTexture classificationand segmentation. Specifically, the following methods will be described: (1) theFeature Extraction (FE)Gray-Level Co-occurrence Matrices (GLCM)gray-level co-occurrence matricesGray-Level Co-occurrence Matrices (GLCM)(GLCM) which is one of the earliest methods for image textureImage textureextraction, (2) GaborFeature Extraction (FE)Gabor filtersfiltersGabor filters, (3) wavelet transformFeature Extraction (FE)Wavelet Transforms (WT)(WT) model and its extensionWavelet Transform (WT), (4) autocorrelationFeature Extraction (FE)autocorrelation functionfunctionAutocorrelation function, (5) Markov random fieldsFeature Extraction (FE)Markov Random Fields (MRF)(MRF)Markov Random Fields (MRF), (6) fractalFeature Extraction (FE)fractal featuresfeaturesFractal features, (7) variogramvariogram, (8) local binary patternLocal Binary Pattern (LBP)(LBP)Feature Extraction (FE)Local Binary Pattern (LBP), and (9) textureFeature Extraction (FE)Texture Spectrum (TS)spectrumTexture Spectrum (TS)(TS). LBP has been frequently used for image textureImage texturemeasure. MRF is a statistical model which has been well studied in image texture analysisImage texture analysisand other applications. There is one common property associated with these methods and models which use the spatial relationship for texture measurement and classification.