Extreme learning machine with autoencoding receptive fields for image classification

Abstract

Based on the theory of local receptive field based extreme learning machine (ELM-LRF) and ELM auto encoder (ELM-AE), a new network structure is proposed to take advantage of global attributes of image and output feature of each layer in the structure. This proposed network structure is called extreme learning machine with autoencoding receptive fields (ELM-ARF), which has two parts including convolution feature extraction and feature coding. In the convolution feature extraction part, local features are extracted using orthogonalized local receptive fields. The ELM-AE theory and local receptive fields are used to encode the global receptive fields, which is used to extract global features. The pooled global features and local features are combined and input into the next layer. In the feature coding part, the shallow layer feature can be input to any deep layer through the ELM-ARF connection structure. A series of encodings are performed on the combined features in each layer to achieve a nonlinear mapping relationship from input information to target categories. In order to verify the validity of the structure, ELM-ARF is tested on four classic databases: USPS, MNIST, NORB and CIFAR10. The experimental results show that ELM-ARF effectively improves image classification accuracy by encoding the combined features that contain global attributes.