A cooperative honey bee mating algorithm and its application in multi-threshold image segmentation

Multilevel thresholding has been one of the most popular image segmentation techniques; however, most of these techniques are time-consuming. This paper proposes a cooperative honey bee mating-based algorithm for natural scenery image segmentation using multilevel thresholding (CHBMA) to save computation time while conquering the curse of dimensionality. The characteristics of natural scenery images are random, imprecise, complicated, and noisy. The locations of interest points in them are not regular. Our proposed algorithm, which is based on honey bee mating algorithms (HBMA) and cooperative learning, considerably enhances the search capability of the algorithm. In the algorithm, we adopt a new population initialization strategy to make the search more efficient, and this strategy works in accordance with the characteristics of multilevel thresholding in an image; the thresholding is arranged from a low gray level to a high gray level. Extensive experiments have demonstrated that our proposed algorithm can deliver more effective and efficient results than state-of-the-art population-based thresholding methods. Thus, our algorithm can be applied in complex image processing, such as automatic target recognition.