Weakly Supervised Multimodal Kernel for Categorizing Aerial Photographs | |
Xia, Yingjie1; Zhang, Luming1; Liu, Zhenguang2; Nie, Liqiang3; Li, Xuelong4; Zhang, Luming (zglumg@gmail.com) | |
作者部门 | 光学影像学习与分析中心 |
2017-08-01 | |
发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING |
ISSN | 1057-7149 |
卷号 | 26期号:8页码:3748-3758 |
产权排序 | 4 |
摘要 | Accurately distinguishing aerial photographs from different categories is a promising technique in computer vision. It can facilitate a series of applications, such as video surveillance and vehicle navigation. In this paper, a new image kernel is proposed for effectively recognizing aerial photographs. The key is to encode high-level semantic cues into local image patches in a weakly supervised way, and integrate multimodal visual features using a newly developed hashing algorithm. The flowchart can be elaborated as follows. Given an aerial photo, we first extract a number of graphlets to describe its topological structure. For each graphlet, we utilize color and texture to capture its appearance, and a weakly supervised algorithm to capture its semantics. Thereafter, aerial photo categorization can be naturally formulated as graphlet-to-graphlet matching. As the number of graphlets from each aerial photo is huge, to accelerate matching, we present a hashing algorithm to seamlessly fuze the multiple visual features into binary codes. Finally, an image kernel is calculated by fast matching the binary codes corresponding to each graphlet. And a multi-class SVM is learned for aerial photo categorization. We demonstrate the advantage of our proposed model by comparing it with state-of-the-art image descriptors. Moreover, an in-depth study of the descriptiveness of the hash-based graphlet is presented. |
文章类型 | Article |
关键词 | Multimodal Categorization Aerial Photograph Image Kernel Weakly-supervised |
WOS标题词 | Science & Technology ; Technology |
DOI | 10.1109/TIP.2016.2639438 |
收录类别 | SCI ; EI |
关键词[WOS] | OBJECT RECOGNITION ; IMAGE CATEGORIES ; FACE RECOGNITION ; HISTOGRAMS ; RERANKING |
语种 | 英语 |
WOS研究方向 | Computer Science ; Engineering |
项目资助者 | National Natural Science Foundation of China(61572169 ; National University of Singapore Suzhou Research Institute ; Zhejiang Provincial Natural Science Foundation of China(LR14F020003) ; 61472266 ; 61472113) |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000403819200008 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/29036 |
专题 | 光谱成像技术研究室 |
通讯作者 | Zhang, Luming (zglumg@gmail.com) |
作者单位 | 1.Zhejiang Univ, Coll Comp Sci, Hangzhou 310027, Peoples R China 2.Natl Univ Singapore, Sch Comp, Singapore 119077, Singapore 3.Shandong Univ, Sch Comp Sci & Technol, Jinan 250100, 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 | Xia, Yingjie,Zhang, Luming,Liu, Zhenguang,et al. Weakly Supervised Multimodal Kernel for Categorizing Aerial Photographs[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2017,26(8):3748-3758. |
APA | Xia, Yingjie,Zhang, Luming,Liu, Zhenguang,Nie, Liqiang,Li, Xuelong,&Zhang, Luming .(2017).Weakly Supervised Multimodal Kernel for Categorizing Aerial Photographs.IEEE TRANSACTIONS ON IMAGE PROCESSING,26(8),3748-3758. |
MLA | Xia, Yingjie,et al."Weakly Supervised Multimodal Kernel for Categorizing Aerial Photographs".IEEE TRANSACTIONS ON IMAGE PROCESSING 26.8(2017):3748-3758. |
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Weakly Supervised Mu(1484KB) | 期刊论文 | 作者接受稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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