Unsupervised object discovery for instance recognition

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00327162" target="_blank" >RIV/68407700:21230/18:00327162 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1109/WACV.2018.00194" target="_blank" >http://dx.doi.org/10.1109/WACV.2018.00194</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/WACV.2018.00194" target="_blank" >10.1109/WACV.2018.00194</a>

Result language

angličtina

Original language name

Unsupervised object discovery for instance recognition

Original language description

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 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 descriptors derived from the salient regions improve particular object retrieval, most noticeably in a large collections containing small objects.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/LL1303" target="_blank" >LL1303: Large Scale Category Retrieval</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018

ISBN

978-1-5386-4886-5

ISSN

2472-6737

e-ISSN

—

Number of pages

10

Pages from-to

1745-1754

Publisher name

Institute of Electrical and Electronics Engineers Inc

Place of publication

—

Event location

Lake Tahoe

Event date

Mar 12, 2018

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000434349200188