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Conference Papers Year : 2014

Image Retrieval with Reciprocal and shared Nearest Neighbors


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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Dates and versions

hal-00907481 , version 1 (06-02-2014)


  • 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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