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Hybrid Diffusion: Spectral-Temporal Graph Filtering for Manifold Ranking

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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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Dates and versions

hal-01909348 , version 1 (31-10-2018)
hal-01909348 , version 2 (31-10-2018)

Identifiers

  • HAL Id : hal-01909348 , version 2

Cite

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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