A stable texture classification approach based on extreme learning machine

For the unstable output of the traditional texture classification methods based on extreme learning machine (ELM), this paper presents a new approach for the automatic classification of texture images. In order to improve the generalization ability and the robustness of the learning model, this paper improves the traditional dynamical model by fusing the linear and nonlinear models together. Due to the faster learning speed, ELM is used as the basic classifier in this paper. Moreover, a proposed dynamical model is utilized 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 according to the classifier agreement. The experimental results on CUReT texture database demonstrate that the proposed approach can improve the stability and classification accuracy significantly, and achieve a more ideal texture classification compared with the traditional methods. ©, 2015, Board of Optronics Lasers. All right reserved.