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To aggregate or not to aggregate: selective match kernels for image search

Giorgos Tolias 1, 2 Yannis Avrithis 2, 3 Hervé Jégou 1 
1 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : This paper considers a family of metrics to compare images based on their local descriptors. It encompasses the VLAD descriptor and matching techniques such as Hamming Embedding. Making the bridge between these approaches leads us to propose a match kernel that takes the best of existing techniques by combining an aggregation procedure with a selective match kernel. Finally, the representation underpinning this kernel is approximated, providing a large scale image search both precise and scalable, as shown by our experiments on several benchmarks.
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Submitted on : Monday, September 23, 2013 - 9:45:55 AM
Last modification on : Wednesday, February 2, 2022 - 3:59:13 PM
Long-term archiving on: : Tuesday, December 24, 2013 - 4:30:27 AM


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  • HAL Id : hal-00864684, version 1


Giorgos Tolias, Yannis Avrithis, Hervé Jégou. To aggregate or not to aggregate: selective match kernels for image search. ICCV - International Conference on Computer Vision, Dec 2013, Sydney, Australia. ⟨hal-00864684⟩



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