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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]
FEL CTU - 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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Submitted on : Wednesday, October 31, 2018 - 10:34:34 AM
Last modification on : Wednesday, November 3, 2021 - 8:13:33 AM


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  • HAL Id : hal-01909348, version 2


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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