A Video Recognition Method by using Adaptive Structural Learning of Long Short Term Memory based Deep Belief Network

Shin Kamada, T. Ichimura
{"title":"A Video Recognition Method by using Adaptive Structural Learning of Long Short Term Memory based Deep Belief Network","authors":"Shin Kamada, T. Ichimura","doi":"10.1109/IWCIA47330.2019.8955036","DOIUrl":null,"url":null,"abstract":"Deep learning builds deep architectures such as multi-layered artificial neural networks to effectively represent multiple features of input patterns. The adaptive structural learning method of Deep Belief Network (DBN) can realize a high classification capability while searching the optimal network structure during the training. The method can find the optimal number of hidden neurons of a Restricted Boltzmann Machine (RBM) by neuron generation-annihilation algorithm to train the given input data, and then it can make a new layer in DBN by the layer generation algorithm to actualize a deep data representation. Moreover, the learning algorithm of Adaptive RBM and Adaptive DBN was extended to the time-series analysis by using the idea of LSTM (Long Short Term Memory). In this paper, our proposed prediction method was applied to Moving MNIST, which is a benchmark data set for video recognition. We challenge to reveal the power of our proposed method in the video recognition research field, since video includes rich source of visual information. Compared with the LSTM model, our method showed higher prediction performance (more than 90% predication accuracy for test data).","PeriodicalId":139434,"journal":{"name":"2019 IEEE 11th International Workshop on Computational Intelligence and Applications (IWCIA)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 11th International Workshop on Computational Intelligence and Applications (IWCIA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWCIA47330.2019.8955036","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

Deep learning builds deep architectures such as multi-layered artificial neural networks to effectively represent multiple features of input patterns. The adaptive structural learning method of Deep Belief Network (DBN) can realize a high classification capability while searching the optimal network structure during the training. The method can find the optimal number of hidden neurons of a Restricted Boltzmann Machine (RBM) by neuron generation-annihilation algorithm to train the given input data, and then it can make a new layer in DBN by the layer generation algorithm to actualize a deep data representation. Moreover, the learning algorithm of Adaptive RBM and Adaptive DBN was extended to the time-series analysis by using the idea of LSTM (Long Short Term Memory). In this paper, our proposed prediction method was applied to Moving MNIST, which is a benchmark data set for video recognition. We challenge to reveal the power of our proposed method in the video recognition research field, since video includes rich source of visual information. Compared with the LSTM model, our method showed higher prediction performance (more than 90% predication accuracy for test data).
基于深度信念网络的长短期记忆自适应结构学习视频识别方法
深度学习构建了多层人工神经网络等深度架构,以有效地表示输入模式的多个特征。深度信念网络(Deep Belief Network, DBN)的自适应结构学习方法可以在训练过程中搜索最优的网络结构,从而实现较高的分类能力。该方法通过神经元生成-湮灭算法找到受限玻尔兹曼机(RBM)的最优隐藏神经元数量,对给定的输入数据进行训练,然后通过层生成算法在受限玻尔兹曼机(RBM)中构造一个新的层,实现深度数据表示。此外,利用LSTM(长短期记忆)的思想,将自适应RBM和自适应DBN的学习算法扩展到时间序列分析中。本文将本文提出的预测方法应用于视频识别的基准数据集Moving MNIST。由于视频包含了丰富的视觉信息来源,因此我们的方法在视频识别研究领域中发挥了巨大的作用。与LSTM模型相比,我们的方法具有更高的预测性能(对测试数据的预测准确率超过90%)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信