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Context-aware vocabulary tree for mobile landmark recognition
Affiliation:1. College of Information Science and Engineering, Ocean University of China, Qingdao, China;2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China;3. Department of Electrical Engineering, Princeton University, NJ 08544, USA;4. Department of Automation, Tsinghua University, Beijing, China;5. Department of Computer Science, Tsinghua University, Beijing, China;6. Department of Computer, Shandong University, Weihai, China;1. Center for Applied Mathematics of Tianjin University, Tianjin 300072, PR China;2. Department of Mathematics, School of Science, Tianjin University, Tianjin 300072, PR China;3. School of Electronic Information Engineering, Tianjin University, Tianjin 300072, PR China;1. Key Lab of Intelligent Information Processing, Institute of Computing technology, Chinese Academy of Sciences, Beijing 100190, China;2. Institute of Digital Media, Peking University, Beijing 100871, China;3. School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen 518005, China;1. School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China;2. School of Electronic Engineering, Xidian University, Xi’an 710071, China;3. School of Computer Engineering, Nanyang Technological University, 639798, Singapore;4. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore;1. School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, PR China;2. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, PR China
Abstract:This paper presents an effective approach that incorporates contextual information into vocabulary tree learning for mobile landmark recognition. For most existing mobile landmark recognition works, the context information (GPS or direction) is mainly used to reduce the search space in a heuristic and insufficient manner. Some recent work uses the context information for codebook learning but only the GPS information is explored. We propose an effective mobile landmark recognition approach which exploits both context (direction and location) and content information for vocabulary tree learning and image recognition. The proposed approach has two major contributions: (i) it proposes an information gain-based codeword discrimination learning method to evaluate the discriminative capability of each direction-aware codeword, as generated by a context-aware vocabulary tree, and (ii) it develops a context-aware image scoring technique based on an inverted file structure that speeds up the image matching process greatly. Experimental results on the NTU and San Francisco database show that the proposed method can achieve good recognition performance with fast speed.
Keywords:Codeword discrimination learning  Location and direction  Inverted file structure  Vocabulary tree  GPS  Direction  Image scoring  Mobile landmark recognition
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