A fast image match method based on water wave optimization and gray relational analysis

Image match has been widely used in computer vision, pattern recognition and image processing. The matching efficiency is a focus topic in the field and some methods have been presented, such as simplification of similarity measure, application of optimization algorithms. Particle swarm optimization algorithm (PSO) has been utilized successfully for image match. However, it is easy to fall into the local optimum and the accuracy isn't good enough. The Water Wave Optimization (WWO) algorithm is a new evolutionary algorithm, which has been proved to be superior to many leading heuristic optimization algorithms on some benchmarking problems and engineering practical problems. In the paper, gray correlation analysis is used to simplify the calculation of similarity measure, and then WWO is employed to obtain the best matching position fast. Experimental results demonstrate that the proposed approach has higher efficiency and matching accuracy than GPSO and GABCA, meanwhile, it has good anti-noise performance.