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Reports (Research Report) Year : 2018

Hybrid Diffusion: Spectral-Temporal Graph Filtering for Manifold Ranking

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-01846002 , version 1 (20-07-2018)

Identifiers

  • HAL Id : hal-01846002 , version 1

Cite

Ahmet Iscen, Yannis Avrithis, Giorgos Tolias, Teddy Furon, Ondřej Chum. Hybrid Diffusion: Spectral-Temporal Graph Filtering for Manifold Ranking. [Research Report] Inria Rennes - Bretagne Atlantique. 2018, pp.1-16. ⟨hal-01846002⟩
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