Image Compression Using Growing Neural Gas

In this paper we study the capacities of characterization and synthesis of objects by using a self-organizing neural model, the Growing Neural Gas. These networks, by means of their competitive learning try to preserve the topology of an input space. This feature is being used for the representation of objects and their movement with topology preserving networks. We characterize the object to be represented by means of the obtained maps and kept information solely on the coordinates and the pixel color of the neurons. With this information it is made the synthesis of the original images, applying mathematical morphology and simple filters using the available information.