Boosting part-sense multi-feature learners toward effective object detection

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	Boosting part-sense multi-feature learners toward effective object detection
	Chen, Shi; Wang, Jinqiao; Ouyang, Yi; Wang, Bo; Xu, Changsheng; Lu, Hanqing
刊名	COMPUTER VISION AND IMAGE UNDERSTANDING
	2011-03-01
卷号	115 期号:3 页码:364-374
关键词	AdaBoost Object detection Multi-feature learners L(1)-regularized gradient boosting
英文摘要	AdaBoost has been applied to object detection to construct the detectors with high performance of discrimination and generalization by single-feature learner. However, the poor discriminative power of extremely weak single-feature learners limits its application for general object detection. In this paper, we propose a novel comprehensive learner design mechanism toward effective object detection in terms of both discrimination and generalization abilities. Firstly, the part-sense multi-feature learners are designed to linearly combine the multiple local features to improve the descriptive and discriminative capacity of the learner. Secondly, we formulate the feature selection in part-sense multi-feature learner as a weighted LASSO regression. Using Least Angle Regression (LARS) method, our approach can choose features adaptively, efficiently and as few as possible to guarantee generalization performance. Finally, a robust L1-regularized gradient boosting is proposed to integrate our part-sense sparse features learner into an object classifier. Extensive experiments and comparisons on the face dataset and the human dataset show the proposed approach outperforms the traditional single-feature learner and other multi-feature learners in discriminative and generalization abilities. (C) 2010 Elsevier Inc. All rights reserved.
WOS标题词	Science & Technology ; Technology
类目[WOS]	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
研究领域[WOS]	Computer Science ; Engineering
关键词[WOS]	FACE DETECTION ; ALGORITHMS
收录类别	SCI
语种	英语
WOS记录号	WOS:000287772400008
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/3326]
专题	自动化研究所_模式识别国家重点实验室_图像与视频分析团队
作者单位	Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Chen, Shi,Wang, Jinqiao,Ouyang, Yi,et al. Boosting part-sense multi-feature learners toward effective object detection[J]. COMPUTER VISION AND IMAGE UNDERSTANDING,2011,115(3):364-374.
APA	Chen, Shi,Wang, Jinqiao,Ouyang, Yi,Wang, Bo,Xu, Changsheng,&Lu, Hanqing.(2011).Boosting part-sense multi-feature learners toward effective object detection.COMPUTER VISION AND IMAGE UNDERSTANDING,115(3),364-374.
MLA	Chen, Shi,et al."Boosting part-sense multi-feature learners toward effective object detection".COMPUTER VISION AND IMAGE UNDERSTANDING 115.3(2011):364-374.