Determining optimal window size for texture feature extraction methods

This paper presents a technique to determine optimal window sizes for the computational methods utilized to extract features from textured images. Those features are useful for both texture classification and segmentation. Window sizes are determined in order to outperform two complementary tasks, namely, texture feature evaluation and texture segmentation, which have opposite requirements. Thus, while texture feature evaluation requires rather large windows in order to obtain meaningful descriptions of their content, texture segmentation requires small windows in order to accurately locate the boundaries between different textured regions. The proposed technique has been applied to several well-known computational methods that are frequently utilized for feature extraction from textured images.