Contextual aerial image categorization using codebook |
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| Affiliation: | 1. Beihang University, Beijing, China;2. Anhui University, Hefei 230601, China;3. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China;1. Beijing Key Lab of Intelligent Telecomm. Software and Multimedia, Beijing University of Posts and Telecomm., Beijing 100876, China;2. School of Information Technology, Deakin University, Melbourne, VIC 3125, Australia;3. Department of Computer Science and Technology, Tsinghua University, Beijing, China;1. Department of Computer Science, University of California, Irvine, USA;2. School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, China;3. Faculty of Information and Communication Technology, Mahidol University, Thailand;1. Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, Guangdong, China;2. University of Virginia, Department of ECE, Charlottesville, VA 22904, USA;3. BJUT Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;1. College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China;2. Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan;1. School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, Guangdong 510006, China;2. Department of Computer Science, Rutgers University, NJ 08854-8019, USA;3. School of Computer Engineering, Nanyang Technological University, 639798 Singapore, Singapore;4. School of Electronic Engineering, Xidian University, Xi’an, Shaanxi 710071, China;5. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, Guangdong 510642, China |
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| Abstract: | Effective categorization of the millions of aerial images from unmanned planes is a useful technique with several important applications. Previous methods on this task usually encountered such problems: (1) it is hard to represent the aerial images’ topologies efficiently, which are the key feature to distinguish the arial images rather than conventional appearance, and (2) the computational load is usually too high to build a realtime image categorization system. Addressing these problems, this paper proposes an efficient and effective aerial image categorization method based on a contextual topological codebook. The codebook of aerial images is learned with a multitask learning framework. The topology of each aerial image is represented with the region adjacency graph (RAG). Furthermore, a codebook containing topologies is learned by jointly modeling the contextual information, based on the extracted discriminative graphlets. These graphlets are integrated into a Bag-of-Words (BoW) representation for predicting aerial image categories. Contextual relation among local patches are taken into account in categorization to yield high categorization performance. Experimental results show that our approach is both effective and efficient. |
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| Keywords: | Aerial image Contextual modeling Efficient algorithm Codebook |
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