Unsupervised texture segmentation using dominant image modulations

We present an unsupervised modulation domain technique for segmenting textured images. A dominant component AM-FM analysis is performed on the image, and estimates of the locally dominant amplitude and frequency modulations are extracted at each pixel. Modulation domain density clustering is then applied to estimate the maximum number of textured regions that might be present in the image. The feature space is augmented with horizontal and vertical spatial information prior to the application of k-means clustering to arrive at an initial image segmentation. Connected components labeling with minor region removal and morphological smoothing are then applied to yield the final segmentation. We demonstrate the technique on several synthetic and natural images.