Large-Scale Aerial Image Categorization Using a Multitask Topological Codebook

doi:10.1109/TCYB.2015.2408592

OPT OpenIR > 光谱成像技术研究室

	Large-Scale Aerial Image Categorization Using a Multitask Topological Codebook
	Zhang, Luming 1; Wang, Meng 1; Hong, Richang 1; Yin, Bao-Cai 2; Li, Xuelong3 ; Wang, M
作者部门	光学影像学习与分析中心
	2016-02-01
发表期刊	IEEE TRANSACTIONS ON CYBERNETICS
ISSN	2168-2267
卷号	46 期号:2 页码:535-545
产权排序	3
摘要	Fast and accurately categorizing the millions of aerial images on Google Maps is a useful technique in pattern recognition. Existing methods cannot handle this task successfully due to two reasons: 1) the aerial images' topologies are the key feature to distinguish their categories, but they cannot be effectively encoded by a conventional visual codebook and 2) it is challenging to build a realtime image categorization system, as some geo-aware Apps update over 20 aerial images per second. To solve these problems, we propose an efficient aerial image categorization algorithm. It focuses on learning a discriminative topological codebook of aerial images under a multitask learning framework. The pipeline can be summarized as follows. We first construct a region adjacency graph (RAG) that describes the topology of each aerial image. Naturally, aerial image categorization can be formulated as RAG-to-RAG matching. According to graph theory, RAG-to-RAG matching is conducted by enumeratively comparing all their respective graphlets (i.e., small subgraphs). To alleviate the high time consumption, we propose to learn a codebook containing topologies jointly discriminative to multiple categories. The learned topological codebook guides the extraction of the discriminative graphlets. Finally, these graphlets are integrated into an AdaBoost model for predicting aerial image categories. Experimental results show that our approach is competitive to several existing recognition models. Furthermore, over 24 aerial images are processed per second, demonstrating that our approach is ready for real-world applications.
文章类型	Article
关键词	Aerial Image Discriminatively Learning Large-scale Multitask Realtime Topology
学科领域	Computer Science, Artificial Intelligence
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TCYB.2015.2408592
收录类别	SCI ; EI
关键词[WOS]	FEATURE-SELECTION ; OBJECT RECOGNITION ; MULTIPLE TASKS ; KERNEL ; INFORMATION ; MULTICLASS ; FEATURES ; MODEL
语种	英语
WOS研究方向	Computer Science
项目资助者	National 973 Program of China(2014CB347600) ; National Natural Science Foundation of China(61272393 ; Program for New Century Excellent Talents in University(NCET-12-0836) ; Key Research Program of Chinese Academy of Sciences(KGZD-EW-T03) ; 61322201 ; 61125106)
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号	WOS:000370962900018
引用统计	被引频次：24[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/181661/27860
专题	光谱成像技术研究室
通讯作者	Wang, M
作者单位	1.Hefei Univ Technol, Comp Sci & Informat Engn Dept, Hefei 230009, Peoples R China 2.Beijing Univ Technol, Sch Transportat, Beijing 100124, Peoples R China 3.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	Zhang, Luming,Wang, Meng,Hong, Richang,et al. Large-Scale Aerial Image Categorization Using a Multitask Topological Codebook[J]. IEEE TRANSACTIONS ON CYBERNETICS,2016,46(2):535-545.
APA	Zhang, Luming,Wang, Meng,Hong, Richang,Yin, Bao-Cai,Li, Xuelong,&Wang, M.(2016).Large-Scale Aerial Image Categorization Using a Multitask Topological Codebook.IEEE TRANSACTIONS ON CYBERNETICS,46(2),535-545.
MLA	Zhang, Luming,et al."Large-Scale Aerial Image Categorization Using a Multitask Topological Codebook".IEEE TRANSACTIONS ON CYBERNETICS 46.2(2016):535-545.

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