Evolving frame splitters by Genetic Programming

This paper extends the application of Genetic Programming into a new area, automatically splitting video frames based on the content. A GP methodology is presented to show how to evolve a program which can analyse the difference between scenes and split them accordingly. The evolved video splitting programs achieve reasonable performance even when the videos are not easily recognizable by eyes due to the server artificial noises. Moreover, a few different approaches have been investigated in this study. We compare the performance of GP with J48, NaïveBayes and one video splitting software, the experimental results show that GP generated splitters are comparable with two conventional machine learning algorithms and more accurate than human written program.