Image Classification And Recognition Based On The Deep Convolutional Neural Network
- DOI
- 10.2991/jimec-17.2017.38How to use a DOI?
- Keywords
- Machine Learning; Computer Vision; Deep Learning; Convolutional Neural Network.
- Abstract
With the development of the information age, there were a lot of data whose features couldn't be extracted or predicted effectively in real life. One of the core function of computer vision technology is to classify and recognize, with classification and recognition as its summit mission of object detection and object positioning. Due to image data were affected by multiple factors such as illumination, environment, angle, certain object features couldn't be established by manual coding, and it is hard for high latitude data in a computer to realize real-time object detection, object localization, classification and recognition. Therefore the higher accuracy in classification could be obtained with the help of GPU of high performance and the large scale pre-training on the super database Image Net, based on the most advanced deep convolution neural network algorithm. The complete optimization training was conducted on data sets Pascal Vision Object Classes (VOC), and the real-time object detection, object localization, classification and recognition were realized by high performance GPU of NVIDIA.
- Copyright
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Yuan-yuan Wang AU - Long-jun Zhang AU - Yang Xiao AU - Jing Xu AU - You-jun Zhang PY - 2017/10 DA - 2017/10 TI - Image Classification And Recognition Based On The Deep Convolutional Neural Network BT - Proceedings of the 2017 2nd Joint International Information Technology, Mechanical and Electronic Engineering Conference (JIMEC 2017) PB - Atlantis Press SP - 171 EP - 174 SN - 2352-538X UR - https://doi.org/10.2991/jimec-17.2017.38 DO - 10.2991/jimec-17.2017.38 ID - Wang2017/10 ER -