Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Photo tourism: exploring photo collections in 3D
ACM SIGGRAPH 2006 Papers
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Large-Scale Discovery of Spatially Related Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Building Rome on a cloudless day
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Total recall II: Query expansion revisited
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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Many new computer vision applications are utilizing large-scale data- sets of places derived from the many billions of photos on the Web. Such applications often require knowledge of the visual connectivity structure of these image collections--describing which images overlap or are otherwise related--and an important step in understanding this structure is to identify connected components of this underlying image graph. As the structure of this graph is often initially unknown, this problem can be posed as one of exploring the connectivity between images as quickly as possible, by intelligently selecting a subset of image pairs for feature matching and geometric verification, without having to test all O(n2) possible pairs. We propose a novel, scalable algorithm called MatchMiner that efficiently explores visual relations between images, incorporating ideas from relevance feedback to improve decision making over time, as well as a simple yet effective rank distance measure for detecting outlier images. Using these ideas, our algorithm automatically prioritizes image pairs that can potentially connect or contribute to large connected components, using an information-theoretic algorithm to decide which image pairs to test next. Our experimental results show that MatchMiner can efficiently find connected components in large image collections, significantly outperforming state-of-the-art image matching methods.