Hybrid Diffusion: Spectral-Temporal Graph Filtering for Manifold Ranking
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00327178" target="_blank" >RIV/68407700:21230/19:00327178 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-030-20890-5_20" target="_blank" >http://dx.doi.org/10.1007/978-3-030-20890-5_20</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-20890-5_20" target="_blank" >10.1007/978-3-030-20890-5_20</a>
Alternative languages
Result language
angličtina
Original language name
Hybrid Diffusion: Spectral-Temporal Graph Filtering for Manifold Ranking
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ACCV 2018: Proceedings of the 14th Asian Conference on Computer Vision, Part II
ISBN
978-3-030-20889-9
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
16
Pages from-to
301-316
Publisher name
Springer
Place of publication
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Event location
Perth
Event date
Dec 4, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000492902300020