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

A technology of convolutional neural network and classification method, applied in the field of image classification based on multi-layer spiking convolutional neural network, can solve the problems of learning non-convergence, voltage redundancy, etc., to reduce the number of overall pulses, ensure convergence, and reduce calculations effect of complexity

Active Publication Date: 2019-08-13
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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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 membra

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

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Embodiment

[0049] An image classification method based on multi-layer spiking convolutional neural network, including the following steps

[0050] Step 1: Convert the images in the training set into pulse sequences, that is, image preprocessing, use the contrast coding method to enhance the edge information of the images and convert them into pulse sequences;

[0051] The step 1 is specifically:

[0052] Step 11: The input is the MNIST digital handwriting image dataset, the image size is 28*28, and the upper bound of the pixel distance of the image is set to d=1 and the maximum time T of neuron pulse firing max =100ms, define the image matrix as A, the pixel value matrix as pixel, both A and pixel are initialized to a 28*28 matrix, and each pixel in the image is p;

[0053] Step 12: Calculate the Euclidean distance of the pixel point p in space, define the pixel point whose Euclidean distance is less than the upper bound of the distance d as q, and add it to the set Γ q , a pixel p cor...

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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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Application Information

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IPC IPC(8): G06K9/62G06N3/04
CPCG06N3/045G06F18/24
Inventor 屈鸿秦展展柏晓祉张亚肖艳清
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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