多层前向人工神经网络图像分类算法  

Algorithm of Multi-layer Forward Artificial Neural Network for Image Classification

作　　者：顾哲彬 曹飞龙 GU Zhe-bin;CAO Fei-long(College of Sciences,China Jiliang University,Hangzhou 310018,China)

机构地区：[1]中国计量大学理学院,杭州310018

出　　处：《计算机科学》2018年第B11期238-243,共6页Computer Science

基　　金：国家自然科学基金(61672477)资助

摘　　要：传统人工神经网络的输入均为向量形式,而图像由矩阵形式表示,因此,在用人工神经网络进行图像处理时,图像将以向量形式输入至神经网络,这破坏了图像的结构信息,从而影响了图像处理的效果。为了提高网络对图像的处理能力,文中借鉴了深度学习的思想与方法,引进了具有矩阵输入的多层前向神经网络。同时,采用传统的反向传播训练算法(BP)训练该网络,给出了训练过程与训练算法,并在USPS手写数字数据集上进行了数值实验。实验结果表明,相对于单隐层矩阵输入前向神经网络(2D-BP),所提多层网络具有较好的分类效果。此外,对于彩色图片分类问题,利用所提出的2D-BP网络,给出了一个有效的可行方法。The input of traditional artificial neural network is in vector form,but the image is represented by matrix.Therefore,in the process of image processing,the image will be inputted into the neural network in vector form,which will destroy the structure information of image,and thus affect the effect of image processing.In order to improve the ability of network on image processing,the multilayer feedforward neural networks with matrix inputs are introduced based on the idea and method of deep learning.At the same time,the traditional back-propagation algorithm(BP)is used to train the network,and the training process and training algorithm are given.After a lot of experiments,the network structure with good performance were determined,and the numerical experiments were carried out on the USPS handwritten digital data set.The experimental results show that the proposed multilayer network has better classification results than the single hidden layer feed forward neural network with matrix input(2D-BP).In addition,to deal with the problem of color image classification,this paper provided an effective and feasible method,the new 2D-BP network,to deal with it.

关 键 词：多层前向神经网络 学习算法 图像分类 梯度优化 

分 类 号：TN911.71[电子电信—通信与信息系统] TP183[电子电信—信息与通信工程]