一种稳定的基于极端学习机的纹理分类方法

针对传统基于极端学习机(ELM)的纹理分类方 法容易出现输出不稳定的缺点,本文将线性和非线 性模型进行融合,改进了传统动力学模型。利用ELM能够快速学习的特点,将其作为基分类 器,同时利用 线性吸引子和局部吸引子的迭代,实现多个ELM分类器的最佳融合,以提升学习模型的泛 化能力和稳定 性。利用动力模型实现多分类器的融合有助于寻求多个基分类器之间的一致性,摒弃了基分 类器中判别错 误的样本输出。通过对CUReT纹理数据库的分类结果,与传统纹理分类方法相 比,本文算法的稳定性 和分类准确率都得到了一定程度的提升,取得了理想的分类效果。

A stable texture classification approach based on extreme learning machine

For the unstable output of the traditional texture classification meth ods based on extreme learning machine (ELM),this paper presents a new approach for the automatic classificat ion of texture images.In order to improve the generalization ability and the robustness of the learning model,thi s paper improves the traditional dynamical model by fusing the linear and nonlinear models together.Due to the fa ster learning speed,ELM is used as the basic classifier in this paper. Moreover,a proposed dynamical model is u tilized to realize the optimal fusion of multiple ELMs with the iteration of linear and local attractor.By combining multiple classifiers with the improved dynamical model,the corrupted classifier outputs are discarded accordin g to the classifier agreement.The experimental results on CUReT texture database demonstrate that the proposed app roach can improve the stability and classification accuracy significantly,and achieve a more ideal texture class ification compared with the traditional methods.