Review of deep convolution neural network in image classification

Al-Saffar, Ahmed Ali Mohammed and Tao, Hai and Mohammed, Ahmed Talab (2017) Review of deep convolution neural network in image classification. International Conference on Radar Antenna, Microwave, Electronics, and Telecommunications. pp. 26-31. ISSN 978-1-5386-3849. (Published)

Preview

Pdf
UMP IR 2 MOHAMMED.PCC15015.FSKKP.pdf
Download (430kB) | Preview

DOI/Official URL: https://www.aconf.org/conf_99389.html

Abstract

With the development of large data age, Convolutional neural networks (CNNs) with more hidden layers have more complex network structure and more powerful feature learning and feature expression abilities than traditional machine learning methods. The convolution neural network model trained by the deep learning algorithm has made remarkable achievements in many large-scale identification tasks in the field of computer vision since its introduction. This paper first introduces the rise and development of deep learning and convolution neural network, and summarizes the basic model structure, convolution feature extraction and pooling operation of convolution neural network. Then, the research status and development trend of convolution neural network model based on deep learning in image classification are reviewed, which is mainly introduced from the aspects of typical network structure construction, training method and performance. Finally, some problems in the current research are briefly summarized and discussed, and the new direction of future development is forecasted

Item Type:	Article
Uncontrolled Keywords:	Deep learning; convolution neural network; image recognition; Image Classification
Subjects:	T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division:	Faculty of Computer System And Software Engineering
Depositing User:	Miss. Ratna Wilis Haryati Mustapa
Date Deposited:	17 Dec 2019 06:40
Last Modified:	17 Dec 2019 06:40
URI:	http://umpir.ump.edu.my/id/eprint/25484
Download Statistic:	View Download Statistics

Actions (login required)

View Item