Local receptive fields based extreme learning machine with hybrid filter kernels for image classification

Abstract

In this paper, an innovative method called extreme learning machine with hybrid local receptive fields (ELM-HLRF) is presented for image classification. In this method, filters generated by Gabor functions and the randomly generated convolution filters are incorporated into the convolution filter kernels of local receptive fields based extreme learning machine (ELM-LRF). Extreme learning machine (ELM) is derived from single hidden layer feed-forward neural networks, and the parameters of its hidden layer can be generated randomly. As locally connected ELM, ELM-LRF directly processes information with strong correlations such as images and speech. In this paper, two main contributions are proposed to improve the classification performance of ELM-LRF. First, the Gabor functions are used as one kind of convolution filter kernels of ELM-HLRF to execute image classification. Second, we use a data augmentation method to preprocess training images to avoid overfitting. Experiments on the Outex texture dataset, the Yale face dataset, the ORL face database and the NORB dataset demonstrate that ELM-HLRF outperforms ELM-LRF, ELM and support vector machine in classification accuracy, and the presented data augmentation method improves the classification performance.