Abstract
Multi-threshold segmentation is a powerful technique that is used for the processing of pattern recognition and computer vision. However, traditional, exhaustive search is computationally expensive when searching for thresholds. In order to solve such challenging problems, the fitness function is designed by the maximum entropy method, the optimal threshold of segmentation is found by using the parallel optimization mechanism of Flower Pollination algorithm (FPA), then a multi-threshold image segmentation algorithm based on FPA is proposed. The experimental results show that FPA is superior to the genetic algorithm (GA) and the shuffled frog leaping algorithm (SFLA).
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