Texture segmentation using local morphological multifractal exponents

This paper deals with the problem of segmenting various textures. For this purpose, we have applied mathematical morphology for the multifractal analysis of images. The digital gray level image is treated as a 3D surface whose multifractal measures are calculated by performing dilations on this surface. Plotting the acquired measures against the size of the structuring element, the local morphological multifractal exponents can be estimated, based on which the unsupervised fuzzy C-means clustering method is used to segment a texture image into the desired number of classes. Randomly choosing 12 natural textures from the Brodatz album, 66 mosaics of 2 textures and 495 mosaics of 4 textures are used to test the new segmentation approach and other two techniques, where the multifractal features are extracted by the box-counting based methods. The comparison results demonstrate that the proposed approach can differentiate texture images more effectively and provide more robust segmentation results.