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题名:
Discriminatively trained part based model armed with biased saliency
作者: Yu, Huapeng1,2,3; Chang, Yongxin1,2,3; Lu, Pei1,2,3; Xu, Zhiyong1; Fu, Chengyu1; Wang, Yafei2
出版日期: 2015
会议名称: Proceedings of SPIE: 20th International Symposium on High Power Systems and Applications 2014, HPLS and A 2014
会议日期: 2015
学科分类: Optical engineering
DOI: 10.1117/12.2064960
通讯作者: Yu, Huapeng
中文摘要: Discriminatively trained Part based Model (DPM) is one of the state-of-the-art object detectors. However, DPM complies little with real vision procedure. In this paper, we try arming DPM with biologically inspired approaches. On the one hand, we use Gabor instead of Histogram of Oriented Gradient (HOG) as low level features to simulate the receptive fields of simple cells. We show Gabor outperforms or is on par with HOG. On the other hand, we learn biased saliency of the object with the same Gabor features to simulate the search procedure of real vision. We combine DPM and biased saliency in a single Bayesian framework, which at least partially reflects the interactions between top-down and bottom-up vision procedures. We show these biologically inspired procedures can effectively improve the performance and efficiency of DPM. We present experimental results on both challenging PASCAL VOC2007 dataset and publicly available sequences. © 2015 SPIE.
英文摘要: Discriminatively trained Part based Model (DPM) is one of the state-of-the-art object detectors. However, DPM complies little with real vision procedure. In this paper, we try arming DPM with biologically inspired approaches. On the one hand, we use Gabor instead of Histogram of Oriented Gradient (HOG) as low level features to simulate the receptive fields of simple cells. We show Gabor outperforms or is on par with HOG. On the other hand, we learn biased saliency of the object with the same Gabor features to simulate the search procedure of real vision. We combine DPM and biased saliency in a single Bayesian framework, which at least partially reflects the interactions between top-down and bottom-up vision procedures. We show these biologically inspired procedures can effectively improve the performance and efficiency of DPM. We present experimental results on both challenging PASCAL VOC2007 dataset and publicly available sequences. © 2015 SPIE.
收录类别: SCI ; Ei
语种: 英语
卷号: 9255
ISSN号: 0277-786X
文章类型: 会议论文
页码: 92553H
Citation statistics:
内容类型: 会议论文
URI标识: http://ir.ioe.ac.cn/handle/181551/7707
Appears in Collections:光电探测与信号处理研究室(五室)_会议论文

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作者单位: 1. Institute of Optics and Electronics, Key Laboratory of Beam Control, Chinese Academy of Sciences, Chengdu, China
2. School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu, China
3. University of Chinese Academy of Sciences, Beijing, China

Recommended Citation:
Yu, Huapeng,Chang, Yongxin,Lu, Pei,et al. Discriminatively trained part based model armed with biased saliency[C]. 见:Proceedings of SPIE: 20th International Symposium on High Power Systems and Applications 2014, HPLS and A 2014. 2015.
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