Selective Multi-Convolutional Region Feature Extraction based Iterative Discrimination CNN for Fine-Grained Vehicle Model Recognition

Yanling Tian, Weitong Zhang, Qieshi Zhang, Gang Lu, Xiaojun Wu

研究成果: Conference contribution

4 被引用数 (Scopus)

抄録

With the rapid rise of computer vision and driverless technology, vehicle model recognition plays a huge role in the common application and industry field. While fine-grained vehicle model recognition is often influenced by multi-level information, such as the image perspective, inter-feature similarity, vehicle details. Furthermore, pivotal regions extraction and fine-grained feature learning have become a vital obstacle to the fine-grained recognition of vehicle models. In this paper, we propose an iterative discrimination CNN (ID-CNN) based on selective multi-convolutional region (SMCR) feature extraction. The SMCR features, which consist of global and local SMCR features, are extracted from the original image with higher activation response value. As for ID-CNN, we use the global and local SMCR features iteratively to localize deep pivotal features and concatenate them together into a fully-connected fusion layer to predict the vehicle categories. We get better results and improve the accuracy to 91.8% on Stanford Cars-196 dataset and to 96.2% on CompCars dataset.

本文言語English
ホスト出版物のタイトル2018 24th International Conference on Pattern Recognition, ICPR 2018
出版社Institute of Electrical and Electronics Engineers Inc.
ページ3279-3284
ページ数6
ISBN(電子版)9781538637883
DOI
出版ステータスPublished - 2018 11 26
外部発表はい
イベント24th International Conference on Pattern Recognition, ICPR 2018 - Beijing, China
継続期間: 2018 8 202018 8 24

出版物シリーズ

名前Proceedings - International Conference on Pattern Recognition
2018-August
ISSN(印刷版)1051-4651

Other

Other24th International Conference on Pattern Recognition, ICPR 2018
CountryChina
CityBeijing
Period18/8/2018/8/24

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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