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题名:
Accurate object detection with a discriminative shape model
作者: Yu, Huapeng1,2,3; Chang, Yongxin1,2,3; Lu, Pei1,2,3; Xu, Zhiyong1; Fu, Chengyu1; Wang, Yafei2
刊名: Optik
出版日期: 2014
卷号: 125, 期号:15, 页码:4102-4107
学科分类: Cost functions - Geometry
DOI: 10.1016/j.ijleo.2014.01.105
通讯作者: Yu, H. (yuhuapeng@uestc.edu.cn)
文章类型: 期刊论文
中文摘要: Discriminative model over bag-of-visual-words representation significantly improves the accuracy of object detection under clutter. However, it encounters bottleneck because of completely ignoring geometric constraint between features. On the contrary, to detect object accurately explicit shape model heavily relies on geometric information of the object, which as a result lacks of discriminative power. In this paper, we present a discriminative shape model to make use of the advantages of the two models based on the insight that the two models are essentially complementary. Discriminative model provides discriminative power, while shape model encodes geometry. The cost function that we used to distinguish objects considers both the detection maps of the discriminative model and the result of shape matching. In this cost function, we adopt a novel way to deal with multi-scale detection maps. We show that this cost function has very strong discriminative power, which makes learning a discriminative threshold for full object detection possible. For shape model, we also present a scheme for learning a good shape model from noisy images. Experiments on UIUC Car and Weizmann-Shotton horses show state-of-the-art performance of our model. © 2014 Elsevier GmbH.
英文摘要: Discriminative model over bag-of-visual-words representation significantly improves the accuracy of object detection under clutter. However, it encounters bottleneck because of completely ignoring geometric constraint between features. On the contrary, to detect object accurately explicit shape model heavily relies on geometric information of the object, which as a result lacks of discriminative power. In this paper, we present a discriminative shape model to make use of the advantages of the two models based on the insight that the two models are essentially complementary. Discriminative model provides discriminative power, while shape model encodes geometry. The cost function that we used to distinguish objects considers both the detection maps of the discriminative model and the result of shape matching. In this cost function, we adopt a novel way to deal with multi-scale detection maps. We show that this cost function has very strong discriminative power, which makes learning a discriminative threshold for full object detection possible. For shape model, we also present a scheme for learning a good shape model from noisy images. Experiments on UIUC Car and Weizmann-Shotton horses show state-of-the-art performance of our model. © 2014 Elsevier GmbH.
收录类别: SCI ; Ei
语种: 英语
WOS记录号: WOS:000339646000065
ISSN号: 00304026
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.ioe.ac.cn/handle/181551/5076
Appears in Collections:光电探测与信号处理研究室(五室)_期刊论文

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作者单位: 1. Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China
2. School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu 610054, China
3. Graduate University of Chinese Academy of Sciences, Beijing 100039, China

Recommended Citation:
Yu, Huapeng,Chang, Yongxin,Lu, Pei,et al. Accurate object detection with a discriminative shape model[J]. Optik,2014,125(15):4102-4107.
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