Auto Image Classification Based on Convolution Neural Network

DOI

10.2991/ncce-18.2018.90How to use a DOI?

Keywords

Vlad; CNN; Vehicle image retrieval; SVM; Traditional feature extraction; SIFT.

Abstract

Aiming at the low accuracy of vehicle image retrieval algorithm based on deep learning, an improved vehicle image classification retrieval model based on convolutional neural network is proposed. According to the complexity of the car image, using convolution neural network to extract the image features from Stanford Cars Dataset database, and use a local feature aggregation descriptor (vector of locally aggregated descriptors, VLAD) to represent a picture. Finally, SVM is used to classify the image of the car. The experimental results show that compared with the traditional visual feature classification algorithm, the accuracy of the model is higher and the retrieval effect is better

Copyright

© 2018, 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/).

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TY  - CONF
AU  - Yong Wang
AU  - Dongdong Shen
AU  - Ying Wang
PY  - 2018/05
DA  - 2018/05
TI  - Auto Image Classification Based on Convolution Neural Network
BT  - Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)
PB  - Atlantis Press
SP  - 565
EP  - 569
SN  - 1951-6851
UR  - https://doi.org/10.2991/ncce-18.2018.90
DO  - 10.2991/ncce-18.2018.90
ID  - Wang2018/05
ER  -