Abstract
The Haralick texture features are common in the image analysis literature, partly because of their simplicity and because their values can be interpreted. It was recently observed that the Haralick texture features are very sensitive to the size of the GLCM that was used to compute them, which led to a new formulation that is invariant to the GLCM size. However, these new features still depend on the sample size used to compute the GLCM, i.e. the size of the input image region-of-interest (ROI).The purpose of this work was to investigate the performance of density estimation methods for approximating the GLCM and subsequently the corresponding invariant features.Three density estimation methods were evaluated, namely a piece-wise constant distribution, the Parzen-windows method, and the Gaussian mixture model. The methods were evaluated on 29 different image textures and 20 invariant Haralick texture features as well as a wide range of different ROI sizes.The results indicate that there are two types of features: those that have a clear minimum error for a particular GLCM size for each ROI size, and those whose error decreases monotonically with increased GLCM size. For the first type of features, the Gaussian mixture model gave the smallest errors, and in particular for small ROI sizes (less than about ).In conclusion, the Gaussian mixture model is the preferred method for the first type of features (in particular for small ROIs). For the second type of features, simply using a large GLCM size is preferred.
Highlights
In their seminal paper from 1973, Haralick et al (1973) introduced 14 texture feature descriptors computed from grey-level co-occurrence matrices (GLCMs, containing the joint frequency of neighbouring pixel greylevels)
Each Haralick feature captures a different aspect of the GLCM, and the features taken together are assumed to capture the characteristics of a particular texture in an image
The other features displayed the behaviour that is clearly seen in figure 1, namely that they either have a clear minimum, or that the relative RMSE decreases monotonically as a function of n
Summary
In their seminal paper from 1973, Haralick et al (1973) introduced 14 texture feature descriptors computed from grey-level co-occurrence matrices (GLCMs, containing the joint frequency of neighbouring pixel greylevels). These were used in the classification of photomicrographs, aerial, and satellite image data. The Haralick texture features were computed as functions of a GLCM. Such features are simple to implement, computed, and result in a set of interpretable and easy-to-understand texture descriptors. Each Haralick feature captures a different aspect of the GLCM, and the features taken together are assumed to capture the characteristics of a particular texture in an image. The texture features are useful when e.g. classifying either whole images, or regions in images, based on texture characteristics
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