深度学习在图像识别中的研究及应用

摘要

对图像进行精确识别，具有非常重要的研究意义，图像识别技术在医药学、航天、军事、工农业等诸多方面发挥着重要的作用。当前图像识别方法大多采用人工提取特征，不仅费时费力，而且提取困难；而深度学习是一种非监督学习，学习过程中可以不知道样本的标签值，整个过程无需人工参与也能提取到好的特征。近年来，将深度学习用于图像识别成为了图像识别领域的研究热点，已取得了良好的效果，并且有广阔的研究空间。

本文基于深度学习在图像识别的相关理论，分析了深度学习的基本模型和方法，并在相关图像数据集上实验论证；另外鉴于深度学习多用于大样本集，本文基于小样本提出了一种改进算法，具体工作内容如下：

（1）分析深度学习中卷积神经网络（Convolutional Neural Networks，CNNs）的基本原理，研究其训练过程和模型结构。其中卷积层通过卷积运算，可以使原信号增强，并且降低噪声，提高信噪比；降采样对卷积层的图像进行子抽样，在保证了有用信息不降低的基础上，减少数据处理量。将其在 MNIST手写字体数据集上进行实验，通过对比分析了该方法和其他经典算法在识别率和时间方面的优劣。

（2）针对于卷积神经网络训练时间过长这一缺陷，分析了深度学习中深度信念网络（Deep Belief Networks，DBNs）的基本原理、训练过程和模型结构。DBNs的分层训练机制大大减少了训练难度，减少了训练时间。引入Softmax作为模型的分类器，将其在MNIST手写字体数据集上进行实验，实验表明：深度信念网络在识别率上和卷积神经网络持平，但训练方法的改善使得消耗时间大大减少。此外，该方法在自然场景CIFAR-10库上也有较好的实验效果。

（3）鉴于深度学习多适用于较大的数据集，针对小样本提出了一种改进的深度信念网络结构：深度信念网络整个过程可以分为预训练和参数微调两个阶段，改进的算法在预训练阶段对样本进行降采样；在参数微调阶段引入随机隐退（Dropout），将隐含层的结点随机清零掉一部分，保持其权重不更新。将改进的模型在MNIST子集和ORL数据集上进行实验，实验表明：在小样本中，引入降采样和随机隐退后，深度信念网络在识别率和耗时方面都有不错的改善，过拟合现象得到有效缓解。

关键字：深度学习图像识别卷积神经网络深度信念网络小样本集

Abstract

Precise recognition for image Has very important research significance, imagerecognition technology is widely used in Medicine , space military ,industry and agriculture.

As now most method of image recognition Used artificial feature extraction which Not only

laborious, but also difficult to extract. Deep Learning is a kind of unsupervised learning, Inthe learning process we need not know the values of samples, The whole process can alsoextracted good characteristics without human participation. in recent years , The deeplearning used in image recognition become the hot research topic in the field of imagerecognition , Has achieved good effect, and have a broad space for research.

In this paper, we based on the study on the theory of image recognition analyzes thedeep learning the basic models and methods ,then do experiment on some image data sets .

Given deep learning more for large sample set , we improved a algorithm proposed to use itinto small sample set, the work can be described as follows:

( 1 ) Analysis basic principles of the convolution neural networks (CNNs) , introducethe training process and model structure of it. The convolution layer can make the originalsignal enhancement, and reduce noise as well as improve signal-to-noise ratio byconvolution operation, use the model into the handwriting data set MNIST, compared toother classical algorithms, analyze their advantages and disadvantages about time andrecognition rate.

( 2 ) aim at the inadequacies of the convolution neural network , Analysis the basicprinciples , training process and model structure of deep belief networks (DBNs). Thestratified training mechanism of DBNs greatly reduces the difficulty and reduces the trainingtime of it. We use Softmax classification system as the classifier , the use this model doexperiment on MNIST datasets , compared to convolution neural network, mainly in therecognition rate and time-consuming ,which can be proved that DBNs has the sameidentifying rate of CNNs, but the elapsed time is greatly reduced, and then analyze thereasons ; addition, use the model into the CIFAR-10 databases , compared to the otheralgorithms .

( 3 ) aim at the deep belief networks algorithm is only applicable to large data sets, raisea improved algorithm of deep belief network aim at small sample set . Before the pre-training , down-sampling of samples , after the training, in the parameter fine-tuning phase ,