Outdoor place recognition using compact local descriptors and multiple queries with user verification

In this paper, we propose a novel method to model outdoor places with compact local descriptors extracted from images taken around geographical places. Region-based and clustering-based methods are used to reduce the number of feature vectors to represent the natural scene images. A Multiple Queries with User Verification (MQUV) scheme is proposed to improve the recognition accuracy and the system reliability. In our application, a mobile phone camera is used to take images around a place and send them back to the server to get relevant information about the place. The MQUV scheme calculates the maximum confidence level of all top 5 matching places and returns the best matching result to the user together with a typical sample image of the recognized place for the user's visual verification. User is suggested to take more images if the system is not confident enough to provide a result. The user can also make one's own decision by visually matching the returned image with the scenery of the place. Experimental results show that the number of feature vectors is significantly reduced with the compact place modeling and the recognition accuracy is improved with the MQUV scheme.