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首页> 外文期刊>International journal of image and data fusion >Improved krill group-based region growing algorithm for image segmentation
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Improved krill group-based region growing algorithm for image segmentation

机译：改进的基于磷虾组的区域增长算法用于图像分割

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摘要

Traditional image segment algorithms have some demerits: edge blur, discontinuous edge is susceptible to peripheral irrelevant information, Poor noise resistance. Therefore, we propose an improved krill group-based region growing algorithm for image segmentation in this paper. First, texture feature of the image is extracted by using Gabor filter. Then, we modify the krill group by the way that the population particle dynamics is divided into two categories: worse fitness value of particle and better fitness value of particle. The inertial weight of worse fitness value of particle is reset to zero, so as to eliminate the adverse effect of inertial weight on the current iteration of the algorithm. The inertial weight of better fitness value particle remains unchanged. The step size scaling factor in the algorithm is treated with non-linear declination to further improve the global exploring ability of the algorithm. Finally, the improved kill group optimisation is used to search for the optimal solution, and the segmentation results are finally obtained. The experimental results show that the proposed image segmentation model can not only improve the segmentation accuracy and reduce the number of iterations but also suppress the background. The contour positioning of the object area is effective.
机译：传统的图像分割算法有一些缺点：边缘模糊，不连续的边缘容易受到外围无关信息的影响，抗噪性差。因此，本文提出了一种改进的基于磷虾组的区域增长算法进行图像分割。首先，使用Gabor滤波器提取图像的纹理特征。然后，我们通过将总体粒子动力学分为两类来修改磷虾组：粒子的适应度值较差，粒子的适应度值较高。将粒子适应性较差的惯性权重重置为零，以消除惯性权重对算法当前迭代的不利影响。适合度更高的粒子的惯性权重保持不变。通过非线性偏斜处理算法中的步长缩放因子，以进一步提高算法的全局探索能力。最后，采用改进的杀伤组优化算法寻找最优解，最终得到分割结果。实验结果表明，提出的图像分割模型不仅可以提高分割精度，减少迭代次数，而且可以抑制背景。对象区域的轮廓定位有效。

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