An Optimization Problem for Evaluation of Image Segmentation Methods

Image segmenting is one of the most important steps in movie and image processing and the machine vision applications. The evaluating methods of image segmenting that recently introduced. In this paper, we proposed a new formulation for the evaluation of image segmentation methods. In this strategy using probabilistic model that utilize the information of pixels (mean and variance) in each region to balance the under-segmentation and over-segmentation. Using this mechanism dynamically set the correlation of pixels in the each region using a probabilistic model, then the evaluation of image segmentation methods introduce for an optimization problem. For solving this problem (evaluation of image segmentation methods) use the novel Imperialist Competitive Algorithm (ICA) that was recently introduced has a good performance in some optimization problems. In this paper a new Imperialist Competitive Algorithm is using chaotic map (CICA2) is proposed. In the proposed algorithm, the chaotic map is used to adapt the radius of colonies movement towards imperialist’s position to enhance the escaping capability from a local optima trap. Some famous benchmarks used to test proposed metric performance. Simulation results show this strategy can improve the performance of the unsupervised evaluation segmentation