Unsupervised Image Segmentation Using New Neuro-Fuzzy Systems

New Neuro-Fuzzy Systems, using algorithms for unsupervised fuzzy clustering based on so-called Weighted Neural Networks, are introduced and used for Unsupervised Image Segmentation. New incremental and fixed (or grid-partitioned) Weighted Neural Networks (WNN) are introduced and used for this purpose. The WNN algorithm (incremental or grid-partitioned) produces a net, of nodes connected by edges, which reflects and preserves the topology of the input data set. Additional weights, which are proportional to the local densities in the input space, are associated with the resulting nodes and edges to store useful information about the topological relations in the given input data set. A fuzziness factor, proportional to the connectedness of the net, is introduced in the system. A watershed-like procedure is used to cluster the resulting net. The number of the resulting clusters is determined by this procedure. The proposed Neuro-Fuzzy Systems are used for Image Segmentation. Experiments underline the usefulness and efficiency of the methods.