Open Access
ARTICLE
A New Sequential Image Prediction Method Based on LSTM and DCGAN
Wei Fang1, 2, Feihong Zhang1, *, Yewen Ding1, Jack Sheng3
1 School of Computer & Software, Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing, 210044, China.
2 State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China.
3 Department of Economics, Finance, Insurance and Risk Management University of Central Arkansas, Conway, 72035, USA.
* Corresponding Author: Feihong Zhang. Email: .
Computers, Materials & Continua 2020, 64(1), 217-231. https://doi.org/10.32604/cmc.2020.06395
Received 18 February 2019; Accepted 08 April 2019; Issue published 20 May 2020
Abstract
Image recognition technology is an important field of artificial intelligence.
Combined with the development of machine learning technology in recent years, it has
great researches value and commercial value. As a matter of fact, a single recognition
function can no longer meet people’s needs, and accurate image prediction is the trend
that people pursue. This paper is based on Long Short-Term Memory (LSTM) and Deep
Convolution Generative Adversarial Networks (DCGAN), studies and implements a
prediction model by using radar image data. We adopt a stack cascading strategy in
designing network connection which can control of parameter convergence better. This
new method enables effective learning of image features and makes predictive models to
have greater generalization capabilities. Experiments demonstrate that our network model
is more robust and efficient in terms of timing prediction than 3DCNN and traditional
ConvLSTM. The sequential image prediction model architecture proposed in this paper is
theoretically applicable to all sequential images.
Keywords
Cite This Article
W. Fang, F. Zhang, Y. Ding and J. Sheng, "A new sequential image prediction method based on lstm and dcgan,"
Computers, Materials & Continua, vol. 64, no.1, pp. 217–231, 2020. https://doi.org/10.32604/cmc.2020.06395
Citations