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• W2924259647 abstract "Feature matching, which refers to establishing reliable correspondence between two sets of features, is a critical prerequisite in a wide spectrum of vision-based tasks. Existing attempts typically involve the mismatch removal from a set of putative matches based on estimating the underlying image transformation. However, the transformation could vary with different data. Thus, a pre-defined transformation model is often demanded, which severely limits the applicability. From a novel perspective, this paper casts the mismatch removal into a two-class classification problem, learning a general classifier to determine the correctness of an arbitrary putative match, termed as Learning for Mismatch Removal (LMR). The classifier is trained based on a general match representation associated with each putative match through exploiting the consensus of local neighborhood structures based on a multiple K -nearest neighbors strategy. With only ten training image pairs involving about 8000 putative matches, the learned classifier can generate promising matching results in linearithmic time complexity on arbitrary testing data. The generality and robustness of our approach are verified under several representative supervised learning techniques as well as on different training and testing data. Extensive experiments on feature matching, visual homing, and near-duplicate image retrieval are conducted to reveal the superiority of our LMR over the state-of-the-art competitors." @default.
• W2924259647 created "2019-04-01" @default.
• W2924259647 creator A5040010053 @default.
• W2924259647 creator A5060407786 @default.
• W2924259647 creator A5087165831 @default.
• W2924259647 creator A5090356888 @default.
• W2924259647 date "2019-08-01" @default.
• W2924259647 modified "2023-10-15" @default.
• W2924259647 title "LMR: Learning a Two-Class Classifier for Mismatch Removal" @default.
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• W2924259647 doi "https://doi.org/10.1109/tip.2019.2906490" @default.
• W2924259647 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30908218" @default.
• W2924259647 hasPublicationYear "2019" @default.
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