Efficient Diffusion on Region Manifolds: Recovering Small Objects with Compact CNN Representations - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2017

Efficient Diffusion on Region Manifolds: Recovering Small Objects with Compact CNN Representations

Abstract

Query expansion is a popular method to improve the quality of image retrieval with both conventional and CNN representations. It has been so far limited to global image similarity. This work focuses on diffusion, a mechanism that captures the image manifold in the feature space. The diffusion is carried out on descriptors of overlapping image regions rather than on a global image descriptor like in previous approaches. An efficient off-line stage allows optional reduction in the number of stored regions. In the on-line stage, the proposed handling of unseen queries in the indexing stage removes additional computation to adjust the precomputed data. We perform diffusion through a sparse linear system solver, yielding practical query times well below one second. Experimentally, we observe a significant boost in performance of image retrieval with compact CNN descriptors on standard benchmarks, especially when the query object covers only a small part of the image. Small objects have been a common failure case of CNN-based retrieval.
Fichier principal
Vignette du fichier
Iscen_cvpr17.pdf (1.57 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01505470 , version 1 (11-04-2017)

Identifiers

  • HAL Id : hal-01505470 , version 1

Cite

Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, Teddy Furon, Ondřej Chum. Efficient Diffusion on Region Manifolds: Recovering Small Objects with Compact CNN Representations. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jul 2017, Honolulu, United States. ⟨hal-01505470⟩
1565 View
676 Download

Share

Gmail Facebook X LinkedIn More