Local Texture Patterns - A Univariate Texture Model for Classification of Images

Texture is an important spatial feature useful for identifying objects or regions of interest in an image. In this paper a new spatial method of texture model is proposed and a comparison of some of the histogram based texture models is carried out. This paper presents a texture analysis operator based on ´local texture patterns (LTP)´. The global image texture is modeled by ´pattern spectrum´ which is an occurrence histogram of LTP over the image. The operator is designed as a gray-scale and rotational invariant texture measure on a local neighborhood. The efficiency of the proposed texture model is tested with classification of images based on log-likelihood similarity measure. The results show that the proposed method provides a very good and robust performance.