A Study Of Texture Segmentation

In recent years, significant progress has been made in image segmentation and classification, but still no global theory of image segmentation exists. The wide variety of segmentation techniques used are basically ad hoc and are very dependent on the way the desired features are presented. The main local features used in various segmentation algorithms are image brightness, color and texture. One of the most important features for image segmentation by the human observer is texture, yet it has been difficult to measure and characterize. Actually, texture segmentation is at a very early stage of development at this time. This paper describes an experimental investigation into digital image segmentation using texture features. Texture features are extracted from a few widely different examples of image data. Several different feature sets are used, and the resulting files are input into an unsupervised clustering algorithm. Several variations on the clustering algorithm are explored: some partition the image into segments by using similarities only in the space of features, and others include spatial information such as the location of individual pixels. The various experimental results are also compared, and a new direction for investigation is described.