Graph-based particular object discovery
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00332224" target="_blank" >RIV/68407700:21230/19:00332224 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/s00138-019-01005-z" target="_blank" >https://doi.org/10.1007/s00138-019-01005-z</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s00138-019-01005-z" target="_blank" >10.1007/s00138-019-01005-z</a>
Alternative languages
Result language
angličtina
Original language name
Graph-based particular object discovery
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>
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
Name of the periodical
Machine Vision and Applications
ISSN
0932-8092
e-ISSN
1432-1769
Volume of the periodical
30
Issue of the periodical within the volume
2
Country of publishing house
DE - GERMANY
Number of pages
12
Pages from-to
243-254
UT code for WoS article
000462151000005
EID of the result in the Scopus database
2-s2.0-85061245675