Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
CRAWDAD: A Community Resource for Archiving Wireless Data at Dartmouth
IEEE Pervasive Computing
Modeling people's place naming preferences in location sharing
Proceedings of the 12th ACM international conference on Ubiquitous computing
Proceedings of the 12th ACM international conference on Ubiquitous computing
Bridging the gap between physical location and online social networks
Proceedings of the 12th ACM international conference on Ubiquitous computing
SensLoc: sensing everyday places and paths using less energy
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
LifeMap: A Smartphone-Based Context Provider for Location-Based Services
IEEE Pervasive Computing
CLR: a collaborative location recommendation framework based on co-clustering
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
PERCOM '11 Proceedings of the 2011 IEEE International Conference on Pervasive Computing and Communications
On the semantic annotation of places in location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Snap and Translate Using Windows Phone
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
Mobility prediction-based smartphone energy optimization for everyday location monitoring
Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
Learning location naming from user check-in histories
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
A unified framework for modeling and predicting going-out behavior
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
Automatically characterizing places with opportunistic crowdsensing using smartphones
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
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A user's location information is commonly used in diverse mobile services, yet providing the actual name or semantic meaning of a place is challenging. Previous works required manual user interventions for place naming, such as searching by additional keywords and/or selecting place in a list. We believe that applying mobile sensing techniques to this problem can greatly reduce user intervention. In this paper, we present an autonomous place naming system using opportunistic crowdsensing and knowledge from crowdsourcing. Our goal is to provide a place name from a person's perspective: that is, functional name (e.g., food place, shopping place), business name (e.g., Starbucks, Apple Store), or personal name (e.g., my home, my workplace). The main idea is to bridge the gap between crowdsensing data from smartphone users and location information in social network services. The proposed system automatically extracts a wide range of semantic features about the places from both crowdsensing data and social networks to model a place name. We then infer the place name by linking the crowdsensing data with knowledge in social networks. Extensive evaluations with real deployments show that the proposed system outperforms the related approaches and greatly reduces user intervention for place naming.