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Self-organizing Fusion Algorithm Applied to Image Segmentation

It presents a novel method called self-organizing fusion (SOF) for performing fast image segmentation. Characteristics of SOF are explored and discussed, both theoretically and empirically. The essence of SOF is that objects are extracted through alternating processes of updating and merging until convergence. Such concurrent updating creates a self-organizing fusion behavior that facilitates identification of regions comprising the same object. The method is computationally efficient as both updating and merging are conducted in parallel fashion, and since parameters selection is done for local regions, it is able to deal with fairly complex images.