Online Image Classification Using Graphics Processing Unit- Based Genetic Programming

A texture classification vision system implemented with Graphics Processing Unit-based genetic programming is described. An online learning environment is implemented, in which genetic programming is automatically invoked when unclassified texture instances are present in an image stream. Once a segment is positively classified, the genetic programming classifier expression is reapplied to frames of the image stream. System performance is enhanced using population-parallel evaluation on a graphics processing unit. Various experiments with textures of varying difficulty were performed. Real-time performance was often seen in cases using 4-segments of texture data, as correct classifiers were evolved within seconds. In all cases, once evolved, classifiers were easily applied to the image stream in real-time. This research shows that high-performance real-time learning environments for image classification are attainable with genetic programming.