为了解决基于深度学习的网格分割方法在训练分割分类器过程中时间消耗大的问题,提出了一种基于蚁狮优化的极限学习机的网格分割方法。利用蚁狮优化算法中蚂蚁种群受精英蚁狮与轮盘赌策略的双重影响,迭代更新蚂蚁种群,将蚁狮种群与蚂蚁种群进行降序全排列,取最优的N个更新蚁狮种群,采用最优蚁狮更新精英蚁狮,保持精英蚁狮为最优解,从而优化极限学习机随机生成的输入权值矩阵与隐层偏置。采用改进的极限学习机方法训练得到一个高精度的分割分类器。以普林斯顿数据集中的6类模型进行实验,结果表明,对于Airplane、Ant、Chair、Octopus、Teddy和Fish模型数据集中训练面片数目为200000~300000的模型,所提方法的训练耗时约为1000 s,且获得了较高的分割精确度,最高分割精确度可达99.49%。

To reduce the time consumption in the training process with mesh segmentation method based on the deep learning, this paper proposed a mesh segmentation based on optimizing extreme learning machine with ant lion optimization. This paper utilized the dual influence of the elite ant lion and roulette strategy in the ant lion optimization algorithm, iteratively updated the ant colony, sorted the ant lion colony and ant colony in descending order, considered the optimal N to update ant lion colony, and used the optimal ant lion to update the elite ant lion to keep the elite ant lion as the optimal solution. Therefore, the input weight matrix and the hidden layer bias randomly generated by the extreme learning machine were optimized, and a high-precision segmentation classifier was obtained using the improved extreme learning machine method. Considering six models in Princeton Shape Benchmark (PSB) dataset, the results show that on the model dataset such as Airplane, Ant, Chair, Octopus, Teddy, and Fish, the training time of the models with the number of faces ranging 200000-300000 is approximately 1000 s. The proposed method has high segmentation accuracy, with the highest segmentation accuracy being 99.49%.