Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Interactive digital photomontage
ACM SIGGRAPH 2004 Papers
High performance imaging using large camera arrays
ACM SIGGRAPH 2005 Papers
Marked-up maps: combining paper maps and electronic information resources
Personal and Ubiquitous Computing
Photo tourism: exploring photo collections in 3D
ACM SIGGRAPH 2006 Papers
Automatic generation of tourist maps
ACM SIGGRAPH 2008 papers
Generating diverse and representative image search results for landmarks
Proceedings of the 17th international conference on World Wide Web
World-scale mining of objects and events from community photo collections
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
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We present a novel data-driven algorithm that leverages online image repositories such as Flickr for automatically generating tourist maps. Our hypothesis is that, given a large enough dataset of images with geo-based metadata, clusters of matching images from that dataset tend to provide reliable cues as to what the popular tourist spots for that location may be. Our algorithm takes the geographical area of interest as input and retrieves geotagged photos from online photo collections. By clustering the photos based on their locations and identifying the popular tags for each cluster, our algorithm generates a set of points of interest (POIs) for the area. After retrieving additional photos based on these discovered POI tags, we use image matching to find the most representative landmark view for each POI. Finally, we remove clutter from the representative image and apply non-photorealistic rendering techniques to generate a map icon for each landmark. We use our system to automatically generate maps showing popular tourist locations for several large cities.