Multi-view Matching for Unordered Image Sets, or "How Do I Organize My Holiday Snaps?"
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Shape Indexing Using Approximate Nearest-Neighbour Search in High-Dimensional Spaces
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
An efficient parts-based near-duplicate and sub-image retrieval system
Proceedings of the 12th annual ACM international conference on Multimedia
iDistance: An adaptive B+-tree based indexing method for nearest neighbor search
ACM Transactions on Database Systems (TODS)
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust multi-view feature matching from multiple unordered views
Pattern Recognition
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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This paper focuses on the multi-view feature matching problem from unordered image sets. Firstly, an efficient and effective high dimensional feature matching algorithm is proposed, so called ELSH (extended local sensitive hash), which can significantly improve matching accuracy at fast speed. Secondly, a novel unsupervised image grouping strategy is proposed to cluster the unordered images into content-related group, which does not normally require any other constraints. Extensive experimental results have shown that our method can obtain better performance than the classical algorithms in tackling multi-view matching problem.