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基于栈自编码器的图像分类器
Authors
曹冬林
李绍滋
+3 more
林丽惠
殷瑞
苏松志
Publication date
28 January 2018
Publisher
Abstract
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VGGflower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.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
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Last time updated on 10/06/2020