Fast Spectral Ranking for Similarity Search - Archive ouverte HAL Access content directly
Conference Papers Year :

Fast Spectral Ranking for Similarity Search

(1) , (2) , (1) , (2) , (1)
1
2

Abstract

Despite the success of deep learning on representing images for particular object retrieval, recent studies show that the learned representations still lie on manifolds in a high dimensional space. This makes the Euclidean nearest neighbor search biased for this task. Exploring the mani-folds online remains expensive even if a nearest neighbor graph has been computed offline. This work introduces an explicit embedding reducing manifold search to Euclidean search followed by dot product similarity search. This is equivalent to linear graph filtering of a sparse signal in the frequency domain. To speed up online search, we compute an approximate Fourier basis of the graph offline. We improve the state of art on particular object retrieval datasets including the challenging Instre dataset containing small objects. At a scale of 10 5 images, the offline cost is only a few hours, while query time is comparable to standard similarity search.
Fichier principal
Vignette du fichier
Iscen_Fast_Spectral_Ranking_CVPR_2018_paper.pdf (1.26 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01839744 , version 1 (16-07-2018)

Identifiers

  • HAL Id : hal-01839744 , version 1

Cite

Ahmet Iscen, Yannis Avrithis, Giorgos Tolias, Teddy Furon, Ondřej Chum. Fast Spectral Ranking for Similarity Search. CVPR 2018 - IEEE Computer Vision and Pattern Recognition Conference, Jun 2018, Salt Lake City, United States. pp.1-10. ⟨hal-01839744⟩
184 View
97 Download

Share

Gmail Facebook Twitter LinkedIn More