Image Segmentation and Recognition using Cellular Evolutionary Networks

Abstract

Image segmentation and image recognition are challenging processes, and the methods of merging those two processes like semantic segmentation have been studied. However, it is a lot of labor to construct the processes of segmentation and recognition manually, so automatic construction of those approaches using machine learning or evolutionary computation have been proposed. In this paper, we propose a model of pixel-wise image segmentation and recognition using Cellular Evolutionary Networks (CEN). Our proposed model is composed of a regular array of the identical feed forward networks, represented in Cartesian Genetic Programming (CGP), and each CGP connects with neighbor CGPs. In addition, we also propose a new model of CEN called CELLular Pyramid (CELLP), which operates multi resolution processing for an input image. We applied CEN and CELLP to some images and verified the effectiveness of our method.