|本期目录/Table of Contents|

[1]林丽惠,殷 瑞,李绍滋 *,等.基于栈自编码器的图像分类器[J].厦门大学学报(自然科学版),2018,57(01):118-123.[doi:10.6043/j.issn.0438-0479.201610021]
 LIN Lihui,YIN Rui,LI Shaozi*,et al.An Image Classifier Based on Stacked Autoencoder[J].Journal of Xiamen University(Natural Science),2018,57(01):118-123.[doi:10.6043/j.issn.0438-0479.201610021]
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《厦门大学学报(自然科学版)》[ISSN:0438-0479/CN:35-1070/N]

卷:
57卷
期数:
2018年01期
页码:
118-123
栏目:
研究论文
出版日期:
2018-01-26

文章信息/Info

Title:
An Image Classifier Based on Stacked Autoencoder
文章编号:
0438-0479(2018)01-0118-06
作者:
林丽惠124殷 瑞13李绍滋13 *苏松志13曹冬林13
1.厦门大学信息科学与技术学院,福建 厦门 361005; 2.武夷学院数学与计算机学院,福建 武夷山 354300; 3.福建省仿脑智能系统重点实验室,福建 厦门 361005; 4.认知计算与智能信息处理福建省高校重点实验室,福建 武夷山 354300
Author(s):
LIN Lihui124YIN Rui13LI Shaozi13*SU Songzhi13CAO Donglin13
1.School of Information Science and Engineering,Xiamen University,Xiamen 361005,China; 2.College of Mathematics and Computer Science,Wuyi University,Wuyishan 354300,China; 3.Fujian Key Laboratory of the Brain-like Intelligent Systems,Xiamen 361005,China
关键词:
图像分类 特征提取 分类器 栈自编码器 卷积神经网络
Keywords:
image classification feature extraction classifier stacked autoencoder convolutional neural network
分类号:
TP 391.4
DOI:
10.6043/j.issn.0438-0479.201610021
文献标志码:
A
摘要:
图像分类问题包含两个重要的部分:特征提取器和分类器.多年来研究人员一直将精力投入到特征表示中,对于分类器却仅进行局部调参.基于一个性能优异的分类器与特征表示对图像分类系统同等重要的思想,提出了基于卷积特征的栈自编码器(stacked autoencoder on convolutional feature maps,SACF)的分类系统,并在数据集 CUB-200和 VGG-flower上进行了实验,对比了SACF与基于卷积特征和多层感知机的卷积神经网络(CNN)分类系统的分类效果,实验结果表明SACF具有更优的分类效果.
Abstract:
In problems of image classification,there are two important components:a feature extractor and a classifier.Researchers have focused on the former for decades,but only for local parameter adjustmentfor classifiers.According to the idea that a good classifier is just as important as the feature representation for the image classification,a classification system based on stacked autoencoder on convolutional feature maps(SACF)is proposed.Experiments are performed based on the CUB-200 and VGG-flower datasets and the classification results of the SACF classification system and the CNN classification system which based on convolutional feature maps and multilayer perceptron are compared.Results show that the accuracy of classification of SACF is superior to that of CNN.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2016-10-28 录用日期:2017-10-31
基金项目:国家自然科学基金(61572409,61571188,61202143); 福建省自然科学基金(2013J05100); 中国乌龙茶产业福建省2011协同创新中心项目(闽教科〔2015〕75号); 福建省教育厅A类科技项目(JA13317)
*通信作者:szlig@xmu.edu.cn
更新日期/Last Update: 1900-01-01