Image Applications of Agglomerative Clustering Using Mixtures of Non-Gaussian Distributions

We present two applications in image processing of an agglomerative clustering method based on mixtures of non-Gaussian distributions. The method joins pair-wise the mixture models estimated for every cluster building a pyramidal or hierarchical structure by using the Kullback-Leibler divergence. This process can be related with the feedforward process of abstraction carried out by the brain. The applications consist of grouping images based on their content similarities and segmentation of regions of an image in similar areas. The capability of the method to distinguish between natural and artificial images is also demonstrated.