Image classification method based on multi-layer spring convolutional neural network

Abstract

The invention discloses an image classification method based on a multilayer spring convolutional neural network, and relates to the field of image processing, and the method comprises the steps: converting images in a training set into pulse sequences; setting a spring neuron parameter, and constructing a convolutional neural network by using a spring neuron; taking the pulse sequence as input, training the convolutional neural network layer by layer, obtaining visual features of the pulse sequence, and obtaining a classification result, the training method being an unsupervised learning algorithm based on a Hebbian rule; converting the to-be-identified image into a pulse sequence, and inputting the pulse sequence into the trained convolutional neural network to obtain a classification result of the to-be-identified image. The technical problem of sparking neuron membrane voltage redundancy calculation caused by increased neuron scale is solved, and meanwhile, the technical problem oflearning non-convergence caused by the fact that an SNN activation function cannot be derived and a back-propagation calculation residual error cannot be used is solved.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an image classification method based on a multi-layer spiking convolutional neural network. Background technique [0002] Image classification is an image processing method that distinguishes objects of different categories according to their different characteristics reflected in the image information. It uses computers to quantitatively analyze images, and classifies each pixel or area in an image or image into one of several categories to replace human visual interpretation. [0003] Spiking neural network (SNN) is a mathematical model based on pulse timing that is highly fitted to biological neural network, and simulates the bioelectrical response of biological neurons through the triggering of pulses. Each neuron is an independent calculation and reaction unit, which is connected through a large-scale parallel network to form a powerful mathematical model and used to complet...

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Problems solved by technology

[0006] The object of the present invention is: the present invention provides a kind of image classification method based on multi-layer spiking convolutional neural network, solves the technical problem of the redundant calculation of spiking neuron membrane voltage due to the increase of neuron scale, solves simultaneously The SNN activation function is not derivable, and it is impossible to use backpropagation to calculate the residual, which leads to technical problems that the learning does not converge

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