Hybrid Diffusion: Spectral-Temporal Graph Filtering for Manifold Ranking

Ahmet Iscen 1 Yannis Avrithis 2 Giorgos Tolias 1 Teddy Furon 2 Ondřej Chum 1
1 VRG - Visual Recognition Group [Prague]
CTU/FEE - Faculty of electrical engineering [Prague]
2 LinkMedia - Creating and exploiting explicit links between multimedia fragments
Inria Rennes – Bretagne Atlantique , IRISA_D6 - MEDIA ET INTERACTIONS
Abstract : State of the art image retrieval performance is achieved with CNN features and manifold ranking using a k-NN similarity graph that is pre-computed off-line. The two most successful existing approaches are temporal filtering, where manifold ranking amounts to solving a sparse linear system online, and spectral filtering, where eigen-decomposition of the adjacency matrix is performed off-line and then manifold ranking amounts to dot-product search online. The former suffers from expensive queries and the latter from significant space overhead. Here we introduce a novel, theoretically well-founded hybrid filtering approach allowing full control of the space-time trade-off between these two extremes. Experimentally , we verify that our hybrid method delivers results on par with the state of the art, with lower memory demands compared to spectral filtering approaches and faster compared to temporal filtering.
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Communication dans un congrès
ACCV 2018 - Asian Conference on Computer Vision, Dec 2018, Perth, Australia. pp.1-16
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https://hal.inria.fr/hal-01909348
Contributeur : Teddy Furon <>
Soumis le : mercredi 31 octobre 2018 - 10:34:34
Dernière modification le : jeudi 15 novembre 2018 - 11:59:01

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  • HAL Id : hal-01909348, version 2

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Ahmet Iscen, Yannis Avrithis, Giorgos Tolias, Teddy Furon, Ondřej Chum. Hybrid Diffusion: Spectral-Temporal Graph Filtering for Manifold Ranking. ACCV 2018 - Asian Conference on Computer Vision, Dec 2018, Perth, Australia. pp.1-16. 〈hal-01909348v2〉

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