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

The problems of multi-threshold image segmentation remain great challenges for image compression, target recognition and computer vision. However, most of them are time-consuming. This paper proposes a cooperative honey bee mating-based algorithm (CHBMA) for image segmentation to save computation time while conquer the curse of dimensionality. CHBMA, based on honey bee mating algorithms (HBMA) and the cooperative learning, greatly enhances the search capability of the algorithm. Moreover, we adopt a new population initialization strategy to make the search more efficient, according to the characters of multilevel thresholding in an image arranged from a low gray level to a high one. Extensive experiments have shown that CHBMA can deliver more effective and efficient results to be applied in complex image processing such as automatic target recognition, compared with state-of-the-art population-based thresholding methods.