Genetic algorithms for video segmentation
The current paper presents a new genetic algorithm (GA)-based method for video segmentation. The proposed method is specifically designed to enhance the computational efficiency and quality of the segmentation results compared to standard GAs. The segmentation is performed by chromosomes that independently evolve using distributed genetic algorithms (DGAs). However, unlike conventional DGAs, the chromosomes are initiated using the segmentation results of the previous frame, instead of random values. Thereafter, only unstable chromosomes corresponding to moving object parts are evolved by crossover and mutation. As such, these mechanisms allow for effective solution space exploration and exploitation, thereby improving the performance of the proposed method in terms of speed and segmentation quality. These advantages were confirmed based on experiments where the proposed method was successfully applied to both synthetic and natural video sequences.
genetic algorithm video segmentation markov random field optimization algorithm genetic operators