Fast texture segmentation using genetic programming
Andy Song, Vic Ciesielski · 2004
This paper presents a method which extends the use of genetic programming (GP) to a complex domain, texture segmentation. By this method, segmentation tasks are performed by texture classifiers which are evolved by the GP. Small cutouts sampled from images of various textures are used for the evolution. The generated classifiers directly use pixel values as input. Based on these classifiers an algorithm which uses a voting strategy to partition texture regions is developed. The results of the investigation indicate that the proposed method is able to accurately identify the boundaries between different texture regions, even if the boundaries are not regular. The method can segment two textures as well as multiple textures. Furthermore, fast segmentation can be achieved. The speed of the proposed texture segmentation method can be a hundred times faster than conventional methods.