A network of dynamically coupled chaotic maps for scene segmentation

In this paper, a computational model for scene segmentation based on a network of dynamically coupled chaotic maps is proposed. Time evolutions of chaotic maps that correspond to an object in the given scene are synchronized with one another, while this synchronized evolution is desynchronized with respect to time evolution of chaotic maps corresponding to other objects in the scene. In this model, the coupling range of each active element increases dynamically according to predefined rules until a saturated state is achieved, i.e., locally coupled chaotic maps corresponding to an object in the initial state will be coupled globally in the final state. Consequently, the advantage of both global coupling and local coupling are incorporated in a single scheme. In comparison to continuous models, this proposed model is suitable for computational implementation. Another significant benefit is that the good performance and transparent dynamics of the model are obtained by utilizing one-dimensional chaotic map instead of complex neuron as each element.