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Machine Vision and Applications

, Volume 30, Issue 2, pp 243–254 | Cite as

Graph-based particular object discovery

  • Oriane Siméoni
  • Ahmet IscenEmail author
  • Giorgos Tolias
  • Yannis Avrithis
  • Ondřej Chum
Special Issue Paper
  • 315 Downloads

Abstract

Severe background clutter is challenging in many computer vision tasks, including large-scale image retrieval. Global descriptors, which are popular due to their memory and search efficiency, are especially prone to corruption by such a clutter. Eliminating the impact of the clutter on the image descriptor increases the chance of retrieving relevant images and prevents topic drift due to actually retrieving the clutter in the case of query expansion. In this work, we propose a novel salient region detection method. It captures, in an unsupervised manner, patterns that are both discriminative and common in the dataset. Saliency is based on a centrality measure of a nearest neighbor graph constructed from regional CNN representations of dataset images. The proposed method exploits recent CNN architectures trained for object retrieval to construct the image representation from the salient regions. We improve particular object retrieval on challenging datasets containing small objects.

Keywords

Image retrieval Unsupervised object discovery Image saliency 

Notes

Acknowledgements

This work was supported by the OP VVV funded project CZ.02.1.01/0.0/0.0/16_019/0000765 “Research Center for Informatics.” The Tesla K40 used for this research was donated by the NVIDIA Corporation.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Inria, Univ Rennes, CNRS, IRISARennesFrance
  2. 2.VRG, FEE, CTU in PraguePragueCzech Republic

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