Image Classification with Densely Sampled Image Windows and Generalized Adaptive Multiple Kernel Learning

doi:10.1109/TCYB.2014.2326596

OPT OpenIR > 光谱成像技术研究室

	Image Classification with Densely Sampled Image Windows and Generalized Adaptive Multiple Kernel Learning
	Yan, Shengye 1; Xu, Xinxing 2; Xu, Dong 2; Lin, Stephen 3; Li, Xuelong4
	2015-03-01
发表期刊	IEEE TRANSACTIONS ON CYBERNETICS
卷号	45 期号:3 页码:395-404
摘要	We present a framework for image classification that extends beyond the window sampling of fixed spatial pyramids and is supported by a new learning algorithm. Based on the observation that fixed spatial pyramids sample a rather limited subset of the possible image windows, we propose a method that accounts for a comprehensive set of windows densely sampled over location, size, and aspect ratio. A concise high-level image feature is derived to effectively deal with this large set of windows, and this higher level of abstraction offers both efficient handling of the dense samples and reduced sensitivity to misalignment. In addition to dense window sampling, we introduce generalized adaptive l(p)-norm multiple kernel learning (GA-MKL) to learn a robust classifier based on multiple base kernels constructed from the new image features and multiple sets of prelearned classifiers from other classes. With GA-MKL, multiple levels of image features are effectively fused, and information is shared among different classifiers. Extensive evaluation on benchmark datasets for object recognition (Caltech256 and Caltech101) and scene recognition (15Scenes) demonstrate that the proposed method outperforms the state-of-the-art under a broad range of settings.
文章类型	Article
关键词	Adapted Classifier Image Classification Multiple Kernel Learning Spatial Pyramid
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TCYB.2014.2326596
收录类别	SCI ; EI
关键词[WOS]	FEATURES ; RECOGNITION ; CONTEXT ; SCALE
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号	WOS:000350146900004
引用统计	被引频次：26[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.opt.ac.cn/handle/181661/24105
专题	光谱成像技术研究室
作者单位	1.Nanjing Univ Informat Sci & Technol, Nanjing 210044, Jiangsu, Peoples R China 2.Nanyang Technol Univ, Sch Comp Engn, Singapore 639798, Singapore 3.Microsoft Res, Beijing 100080, Peoples R China 4.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr Opt Imagery Anal & Learning, Xian 710119, Peoples R China
推荐引用方式 GB/T 7714	Yan, Shengye,Xu, Xinxing,Xu, Dong,et al. Image Classification with Densely Sampled Image Windows and Generalized Adaptive Multiple Kernel Learning[J]. IEEE TRANSACTIONS ON CYBERNETICS,2015,45(3):395-404.
APA	Yan, Shengye,Xu, Xinxing,Xu, Dong,Lin, Stephen,&Li, Xuelong.(2015).Image Classification with Densely Sampled Image Windows and Generalized Adaptive Multiple Kernel Learning.IEEE TRANSACTIONS ON CYBERNETICS,45(3),395-404.
MLA	Yan, Shengye,et al."Image Classification with Densely Sampled Image Windows and Generalized Adaptive Multiple Kernel Learning".IEEE TRANSACTIONS ON CYBERNETICS 45.3(2015):395-404.

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