|Table of Contents|

Image Recognition Based on Improved Deep Neural Network(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2019年03期
Page:
115-121
Research Field:
·全国机器学习会议论文专栏·
Publishing date:

Info

Title:
Image Recognition Based on Improved Deep Neural Network
Author(s):
Tang Kai; He Qing; Zhao Qun; Wang Xu
College of Big Data and Information Engineering,Guizhou University,Guizhou Provincial Key Laboratory of Public Big Data,Guiyang 550025,China
Keywords:
image classificaiton; deep learning; deep residual network; spatial transformation network
PACS:
TP183
DOI:
10.3969/j.issn.1001-4616.2019.03.015
Abstract:
With the development of big data era,deep learning has gradually become more practical,leading the development of the era of artificial intelligence. Convolution neural network plays a very important role in image recognition,and it is one of the important components of deep learning model.The key point of image recognition is how to extract the effective features of the image,so as to effectively solve the problem of image recognition. In view of this difficulty,the main work of this paper is to introduce spatial transformation network on the basis of residual network(ResNet). The spatial transformation network can effectively extract the region of interest and improve the efficiency of image recognition. At the same time for the feature extracted by Softmax classifier is not good. In many cases,the intra-class spacing is even larger than the inter-class spacing,but in the image recognition task,the expected features are not only divisible,and with require great differences. In order to solve this problem,this paper introduces the Center Loss function into the Softmax classifier. Center Loss function can make the distance between the extracted feature classes larger and the intra-class distance smaller,thus improving the recognition degree of the extracted features. In the open CIFAR10 dataset,the model has achieved good performance,and the correct recognition rate is up to 89%. Under the same experimental conditions,compared with the unmodified residual network model,the proposed model improves the recognition accuracy of open CIFAR10 dataset by 6%.

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Last Update: 2019-09-30