The Six Components of Tamura Texture Features

The six components of Tamura texture features correspond to six perceptual texture attributes from a psychological perspective: coarseness, contrast, directionality, linelikeness, regularity, and roughness. Among these, the first three components are particularly crucial for image retrieval applications. The MATLAB toolkit contains both sub-functions and main functions for calculating Tamura texture features, featuring modular implementation that separates individual feature computations from the main processing workflow. Tamura texture features represent a method for describing image textures by calculating attributes such as coarseness, contrast, directionality, linelikeness, regularity, and roughness. These attributes play vital roles in image retrieval, with coarseness, contrast, and directionality being especially important. The MATLAB implementation includes specialized functions for each component: coarseness calculation using multi-scale window averaging, contrast computation through statistical moment analysis, directionality measurement via gradient orientation histograms, and additional algorithms for the remaining features. Using this toolkit, you can efficiently compute Tamura texture features for images and apply them to areas like image retrieval and pattern recognition. The integration of main functions (handling overall workflow coordination) and sub-functions (managing specific feature calculations) simplifies the computation process while maintaining computational efficiency through optimized matrix operations and vectorized implementations. If you're working with image texture analysis, this toolkit provides valuable computational resources with clear function interfaces and comprehensive documentation for straightforward integration into your research or application development projects.