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Image Retrieval with Reciprocal and shared Nearest Neighbors

Agni Delvinioti 1 Hervé Jégou 1 Laurent Amsaleg 1 Michael E. Houle 2 
1 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : Content-based image retrieval systems typically rely on a similarity measure between image vector representations, such as in bag-of-words, to rank the database images in decreasing order of expected relevance to the query. However, the inherent asymmetry of k-nearest neighborhoods is not properly accounted for by traditional similarity measures, possibly leading to a loss of retrieval accuracy. This paper addresses this issue by proposing similarity measures that use neighborhood information to assess the relationship between images. First, we extend previous work on k-reciprocal nearest neighbors to produce new measures that improve over the original primary metric. Second, we propose measures defined on sets of shared nearest neighbors for re-ranking the shortlist. Both these methods are simple, yet they significantly improve the accuracy of image search engines on standard benchmark datasets.
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Submitted on : Thursday, February 6, 2014 - 12:02:47 PM
Last modification on : Tuesday, July 5, 2022 - 8:38:23 AM
Long-term archiving on: : Tuesday, May 6, 2014 - 10:06:00 PM


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  • HAL Id : hal-00907481, version 1


Agni Delvinioti, Hervé Jégou, Laurent Amsaleg, Michael E. Houle. Image Retrieval with Reciprocal and shared Nearest Neighbors. VISAPP--International Conference on Computer Vision Theory and Applications, Jan 2014, Barcelone, Portugal. ⟨hal-00907481⟩



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