Abstract
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.
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