CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Learning to recognition from Bing Clickture data
Li, Chenghua1,2; Song, Qiang1,2; Wang, Yuhang1,2; Song, Hang1; Kang, Qi4; Cheng, Jian1,2,3; Lu, Hanqing1,2
2016
Conference NameIEEE International Conference on Multimedia & Expo Workshops
Conference Date2016.7.11-7.15
Conference PlaceSeattle, USA
AbstractMSR Image Recognition Challenge (IRC) 2016 tries to deal with designing a dog breeds recognition system based on Clickture-Dog dataset. To address the task, we proposed an effective method with systematic strategies as follows. We presented a data cleaning method using faster-rcnn to learn a dog detector. Besides, we ensembeled a series of CNN models to enhance the robustness. A dense evaluation strategy was carried out to boost the test accuracy. Finally, we achieved the first prize of this challenge with 89.65% top-5 accuracy. 
KeywordClickture Data Cnns Recognition
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20139
Collection模式识别国家重点实验室_图像与视频分析
Affiliation1.Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2.University of Chinese Academy of Sciences
3.Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences
4.Beijing Institute Of Technology
Recommended Citation
GB/T 7714
Li, Chenghua,Song, Qiang,Wang, Yuhang,et al. Learning to recognition from Bing Clickture data[C],2016.
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