Unsupervised object discovery for instance recognition

Oriane Siméoni 1 Ahmet Iscen 1 Giorgos Tolias 2 Yannis Avrithis 1 Ondřej Chum 2
1 LinkMedia - Creating and exploiting explicit links between multimedia fragments
Inria Rennes – Bretagne Atlantique , IRISA-D6 - MEDIA ET INTERACTIONS
2 VRG - Visual Recognition Group [Prague]
CTU/FEE - Faculty of electrical engineering [Prague]
Abstract : Severe background clutter is challenging in many computer vision tasks, including large-scale image retrieval. Global descriptors, that 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 discrim-inative 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 descriptors derived from the salient regions improve particular object retrieval, most noticeably in a large collections containing small objects.
Document type :
Preprints, Working Papers, ...
Complete list of metadatas

Cited literature [46 references]  Display  Hide  Download

https://hal.inria.fr/hal-02370223
Contributor : Yannis Avrithis <>
Submitted on : Tuesday, November 19, 2019 - 12:58:14 PM
Last modification on : Thursday, November 21, 2019 - 1:21:05 AM

File

1709.04725.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-02370223, version 1
  • ARXIV : 1709.04725

Citation

Oriane Siméoni, Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, Ondřej Chum. Unsupervised object discovery for instance recognition. 2017. ⟨hal-02370223⟩

Share

Metrics

Record views

12

Files downloads

33