Improved Geometric Verification for Large Scale Landmark Image Collections
published: Oct. 9, 2012, recorded: September 2012, views: 3052
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In this work, we address the issue of geometric verification, with a focus on modeling large-scale landmark image collections gathered from the internet. In particular, we show that we can compute and learn descriptive statistics pertaining to the image collection by leveraging information that arises as a by-product of the matching and verification stages. Our approach is based on the intuition that validating numerous image pairs of the same geometric scene structures quickly reveals useful information about two aspects of the image collection: (a) the reliability of individual visual words and (b) the appearance of landmarks in the image collection. Both of these sources of information can then be used to drive any subsequent processing, thus allowing the system to bootstrap itself. While current techniques make use of dedicated training/preprocessing stages, our approach elegantly integrates into the standard geometric verification pipeline, by simply leveraging the information revealed during the verification stage. The main result of this work is that this unsupervised “learning-as-you-go” approach significantly improves performance; our experiments demonstrate significant improvements in efficiency and completeness over standard techniques.
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