Large-Scale Aerial Image Categorization Using a Multitask Topological Codebook
文献类型：期刊论文
| 作者 | Zhang, Luming1; Wang, Meng1; Hong, Richang1; Yin, Bao-Cai2; Li, Xuelong3
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| 刊名 | ieee transactions on cybernetics
 |
| 出版日期 | 2016-02-01 |
| 卷号 | 46期号:2页码:535-545 |
| 关键词 | Aerial image discriminatively learning large-scale multitask realtime topology |
| ISSN号 | 2168-2267 |
| 产权排序 | 3 |
| 通讯作者 | wang, m |
| 英文摘要 | 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. |
| WOS标题词 | science & technology ; technology |
| 学科主题 | computer science, artificial intelligence ; computer science, cybernetics |
| 类目[WOS] | computer science, artificial intelligence ; computer science, cybernetics |
| 研究领域[WOS] | computer science |
| 关键词[WOS] | feature-selection ; object recognition ; multiple tasks ; kernel ; information ; multiclass ; features ; model |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000370962900018 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/27860]  |
| 专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
| 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.(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. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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