Competition system for automatically evaluating edge detection algorithms based on the evolutionary game theory

An ensemble learning algorithm based on game theory is proposed to evaluate algorithms of image analysis and image feature extraction.A competition system is established to implement the algorithm for evaluating the applicability and efficiency of different edge detection algorithms.Through the game in the algorithm competition system,the most suitable algorithm as a winner in the competition can be selected.A group of optimal parameters for the corresponding edge detection can also be found.Firstly,based on the evolutionary game theory,a strategy of the competition of edge extraction algorithms is developed.Secondly,after selecting the most suitable algorithm from the candidates,the overall parameters are optimized.Experiments show that for a specific class of images,several candidate algorithms can be used as a class of preference algorithms based on the final evolutionary result.When analyzing the images,the priority algorithm can be recommended as the best edge detection algorithm from these reference algorithms.It is more effective than traditional methods in determining an algorithm and choosing parameters.