Abstract
The main objective in content-based image retrieval is to find images similar to a query image in an image collection. Matching using descriptors computed from regions centered at local invariant interest points (keypoints) have become popular because of their robustness to changes in viewpoint and occlusion. However, local descriptor matching can produce many false matches. To improve the retrieval results, geometric verification is usually performed as a post-pocessing step. RANSAC can robustly fit a model to data in presence of outliers and has been widely used for the geometric verification stage. But obtaining a good hypothesis may require many trial runs, particularly when the proportion of inliers in the data is low. We introduce a novel geometric verification scheme called DT-RANSAC based on topological information in the Delaunay Triangulation of putatively matched keypoints to construct a refined set of matches, that is presented to the RANSAC algorithm to fit a homography. Experiments reveal that DT-RANSAC is able to converge to correct hypothesis in very few trial runs and the retrieval results are consistently better than geometric verification based on plain RANSAC.
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Bhattacharya, P., Gavrilova, M. (2013). DT-RANSAC: A Delaunay Triangulation Based Scheme for Improved RANSAC Feature Matching. In: Gavrilova, M.L., Tan, C.J.K., Kalantari, B. (eds) Transactions on Computational Science XX. Lecture Notes in Computer Science, vol 8110. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41905-8_2
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DOI: https://doi.org/10.1007/978-3-642-41905-8_2
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