基于卷积神经网络的少数民族头饰识别

doi:10.16180/j.cnki.issn1007-7820.2019.02.011

电子科技 ›› 2019, Vol. 32 ›› Issue (2): 51-55.doi: 10.16180/j.cnki.issn1007-7820.2019.02.011

基于卷积神经网络的少数民族头饰识别

李荣瑞,施霖,赵薇

昆明理工大学信息工程与自动化学院,云南昆明 650500

收稿日期:2018-01-22 出版日期:2019-02-15 发布日期:2019-01-02
作者简介:李荣瑞(1992-),男,硕士研究生。研究方向:图像处理,深度学习。|施霖(1972-),男,博士,副教授。研究方向:计算机视觉,计算机图形学。|赵薇(1993-),女,硕士研究生。研究方向:计算机视觉,深度学习。
基金资助:
云南省人才培养基金(KKSY201303074)

Minority Headdress Recognition Based on Convolutional Neural Network

LI Rongrui,SHI Lin,ZHAO Wei

Faculty of Information Engineering and Automation, Kunming University of Science and Technology,Kunming 650500, China

Received:2018-01-22 Online:2019-02-15 Published:2019-01-02
Supported by:
Yunnan Talent Training Fund(KKSY201303074)

摘要/Abstract

摘要：

传统头饰图片识别方法的特征点由研究人员手工提取,工作量大且准确率低,识别系统存在预处理步骤繁琐、样本要求高等缺点。针对上述问题,文中通过构建卷积神经网络从大量图片数据中自动学习头饰图片的深层特征。文中的CNN模型选用稀疏性较好的ReLU激活函数调整输出,利用反向传播算法(BP算法)优化网络参数,在训练得到的CNN模型后接Softmax分类器进行识别。实验结果表明,系统对头饰图片测试集的识别率达到96.25%,具有良好的识别准确率和识别效率。

关键词: 卷积神经网络, 少数民族头饰, 特征提取, 图像识别, 深度学习, Caffe

Abstract:

The feature point of the traditional headdress image recognition method was extracted by the researchers manually. The system has some disadvantages including tedious preprocessing steps, high sample requirement and low accuracy. To solve these problems, the convolutional neural network was constructed to learn the deep features from image data. The CNN model selected the ReLU function with better sparsity to adjust the output, and used back propagation algorithm to optimize the network parameters.The softmax classifier was identified after the CNN model. The experimental results showed that the recognition rate of the system to the test set of headdress reached 96.25%. This method was proved to have good recognition accuracy and recognition efficiency.

Key words: CNN, minority headdress, feature extraction, image recognition, deep learning, Caffe

中图分类号:

TP391

李荣瑞,施霖,赵薇. 基于卷积神经网络的少数民族头饰识别[J]. 电子科技, 2019, 32(2): 51-55.

LI Rongrui,SHI Lin,ZHAO Wei. Minority Headdress Recognition Based on Convolutional Neural Network[J]. Electronic Science and Technology, 2019, 32(2): 51-55.

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民族	测试数量	准确率
俄罗斯族	15	80%
苗族	28	92.9%
傈僳族	23	86.9%
水族	27	96.3%
其余8类	147	100%

基于卷积神经网络的少数民族头饰识别

Minority Headdress Recognition Based on Convolutional Neural Network

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