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题名:
Efficient object detection based on selective attention
作者: Yu, Huapeng1,2,3; Chang, Yongxin1,2,3; Lu, Pei1,3; Xu, Zhiyong1; Fu, Chengyu1; Wang, Yafei2
刊名: COMPUTERS & ELECTRICAL ENGINEERING
出版日期: 2014-04-01
卷号: 40, 期号:3, 页码:907-919
文章类型: Article
英文摘要: In this paper, we make use of biologically inspired selective attention to improve the efficiency and performance of object detection under clutter. At first, we propose a novel bottom-up attention model. We argue that heuristic feature selection based on bottom-up attention can stably select out invariant and discriminative features. With these selected features, performance of object detection can be improved apparently and stably. Then we propose a novel concept of saccade map based on bottom-up attention to simulate the saccade (eye movements) in vision. Sliding within saccade map to detect object can significantly reduce computational complexity and apparently improve performance because of the effective filtering for distracting information. With these ideas, we present a general framework for object detection through integrating bottom-up attention. Through evaluating on UIUC cars and Weizmann-Shotton horses we show state-of-the-art performance of our object detection model. (C) 2013 Elsevier Ltd. All rights reserved.
WOS标题词: Science & Technology ; Technology
类目[WOS]: Computer Science, Hardware & Architecture ; Computer Science, Interdisciplinary Applications ; Engineering, Electrical & Electronic
研究领域[WOS]: Computer Science ; Engineering
关键词[WOS]: VISUAL-ATTENTION ; CORTEX ; RECOGNITION ; MODEL
收录类别: SCI
语种: 英语
WOS记录号: WOS:000336187000014
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.ioe.ac.cn/handle/181551/2473
Appears in Collections:光电技术研究所被WoS收录文章_期刊论文

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作者单位: 1.Chinese Acad Sci, Inst Opt & Elect, Chengdu 610209, Sichuan Provinc, Peoples R China
2.Univ Elect Sci & Technol China, Sch Optoelect Informat, Chengdu 610054, Peoples R China
3.Grad Univ Chinese Acad Sci, Chengdu 610054, Peoples R China

Recommended Citation:
Yu, Huapeng,Chang, Yongxin,Lu, Pei,et al. Efficient object detection based on selective attention[J]. COMPUTERS & ELECTRICAL ENGINEERING,2014,40(3):907-919.
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