Evolutional RBFNs image model describing-based segmentation system designs

Knowledge discovered-based radial basis function neural networks (RBFNs) model can describe an appropriate behaviors of identified image patterns through the multiple and hybrid learning schemes. The image data extraction learning algorithm (IDELA) with dynamic recognitions to automatically match the appropriate feature with a suitable number of radial basis function (RBFs). This first step approaches their associated centers positions to extract initial prototypes. The approximated image model as a describer is automatically generated by the RBFPSO learning scheme, which is contained hybrid bacterial foraging particle swarm optimization (BFPSO) algorithm and recursive least-squares (RLS) iterations to deeply approach the image feature. Due to the limitations and possible local learning trap, K-means, differential evolution (DE) and particle swarm optimization (PSO) learning algorithms cannot obtain the most smaller Root-Mean-Square Error (RMSE) to achieve an appropriate image segmentation in all experiment cases. The constructed RBFNs image model is generated by the support of multiple image self-extraction feature machine (MISEFM), which combined IDELA and RBFPSO algorithms to develop the universal RBFNs image describers. Simulations compared with other K-means, PSO and DE learning methods, show the average great performance in several real image segmentation applications. The peak signal-to-noise ratio (PSNR) index is selected to evaluate the quality of the reconstructed images. Simulations show that the evolutional hybrid and multi-level RBFNs image model-based system is determined to simultaneously achieve both high performance indexes on accuracy (RMSE) and a high image quality description (PSNR) for matching the desired characters and behaviors of image patterns within a fewer RBFs functions.