Segmentation of textured images based on fractals and image filtering
This paper describes a new approach to the segmentation of textured gray-scale images based on image pre-filtering and fractal features. Traditionally, filter bank decomposition methods consider the energy in each band as the textural feature, a parameter that is highly dependent on image intensity. In this paper, we use fractal-based features which depend more on textural characteristics and not intensity information. To reduce the total number of features used in the segmentation, the significance of each feature is examined using a test similar to the F-test, and less significant features are not used in the clustering process. The commonly used K-means algorithm is extended to an iterative K-means by using a variable window size that preserves boundary details. The number of clusters is estimated using an improved hierarchical approach that ignores information extracted around region boundaries.
fractal features k-means texture segmentation gabor filters energy features