Image receptive fields for artificial neural networks
This paper describes the structure of the Image Receptive Fields Neural Network (IRF-NN) introduced recently by our team. This structure extends simplified learning introduced by Extreme Learning Machine and Reservoir Computing techniques to the field of images. Neurons are organized in a single hidden layer feedforward network architecture with an original organization of the network?s input weights. To represent color images efficiently, without prior feature extraction, the weight values linked to a neuron are determined by a 2-D Gaussian function. The activation of a neuron by an image presents the properties of a nonlinear localized receptive field, parameterized with a small number of degrees of freedom. A network composed of a large number of neurons, each associated with a randomly initialized and constant receptive field, induces a remarkable representation of the images. Supervised training determines only the output weights of the network. It is therefore extremely fast, without retropropagation or iterations, adapted to large sets of images. The network is easy to implement, presents excellent generalization performances for classification applications, and allows the detection of unknown inputs. The efficiency of this technique is illustrated with several benchmarks, photo and video datasets.
extreme learning machine computer vision visual object recognition artificial neural networks model supervised learning algorithm visual receptive fields