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Most approaches to large-scale image retrieval are based on the constructionof the inverted index of local image descriptors or visual words. A search insuch an index usually results in a large number of candidates. This list ofcandidates is then re-ranked with the help of a geometric verification, using aRANSAC algorithm, for example. In this paper we propose a featurerepresentation, which is built as a combination of three local descriptors. Itallows one to significantly decrease the number of false matches and to shortenthe list of candidates after the initial search in the inverted index. Thiscombination of local descriptors is both reproducible and highlydiscriminative, and thus can be efficiently used for large-scale near-duplicateimage retrieval.

Large scale near-duplicate image retrieval using Triples of Adjacent Ranked Features (TARF) with embedded geometric information