一种改进的深度卷积神经网络的精细图像分类  

An Improved Depth Convolutional Neural Network for Fine Image Classification

作　　者：杨国亮 王志元 张雨 

机构地区：[1]江西理工大学电气工程与自动化学院,江西赣州341000

出　　处：《江西师范大学学报（自然科学版）》2017年第5期473-480,共8页Journal of Jiangxi Normal University(Natural Science Edition)

基　　金：国家自然科学基金(51365017;61305019)资助项目

摘　　要：精细图像分类不同于传统的图像分类,由于精细图像自身的类间相似性和类内差异性,传统的基于手工特征和局部特征组合方法已经很难完整地表达精细图像的特征,因此提出了一种基于改进的深度卷积神经网络模型.由于深度卷积神经网络结构参数和神经元数量巨大,训练模型困难,所以采用高斯分布对前6层参数初始化,其中激活函数采用校正之后的Relus-Softplus函数,在花卉图像数据库OXford-102flowers中TOP1准确率达到85.75%,TOP3准确率达到了94.50%.实验结果表明:该模型在中等规模数据集上比传统方法优势明显,且比未改进的CNN模型识别率高.Fine image classification is different from traditional image classification. Due to the similarity between intraclass and intraclass differences of fine-grained images themselves,it is difficult to express the characteristics of fine image based on manual feature and local feature combination method. Based on the improved depth convolution neural network model,due to the large number of deep convolution neural network structure parameters and the large number of neurons,the training model is difficult,and the Gaussian distribution is used to initialize the first six parameters. The activation function is used after the correction of the Relus-Softplus function,the TOP1 accuracy rate of the flower image database OXford-102 flowers is 85. 75%,and the TOP3 accuracy rate is 94. 50%. The experimental results show that the model has obvious advantages over the traditional method,and the recognition rate is higher than that of the unmodified CNN model.

关 键 词：精细图像分类 深度卷积神经网络 激活函数 特征提取 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]