Improving Image Pair Selection for Large Scale Structure from Motion by Introducing Modified Simpson Coefficient
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F22%3A00364315" target="_blank" >RIV/68407700:21730/22:00364315 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1587/transinf.2021EDP7244" target="_blank" >https://doi.org/10.1587/transinf.2021EDP7244</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1587/transinf.2021EDP7244" target="_blank" >10.1587/transinf.2021EDP7244</a>
Alternative languages
Result language
angličtina
Original language name
Improving Image Pair Selection for Large Scale Structure from Motion by Introducing Modified Simpson Coefficient
Original language description
Selecting visually overlapping image pairs without any prior information is an essential task of large-scale structure from motion (SfM) pipelines. To address this problem, many state-of-the-art image re trieval systems adopt the idea of bag of visual words (BoVW) for com puting image-pair similarity. In this paper, we present a method for im proving the image pair selection using BoVW. Our method combines a conventional vector-based approach and a set-based approach. For the set similarity, we introduce a modified version of the Simpson (m-Simpson) coefficient. We show the advantage of this measure over three typical set similarity measures and demonstrate that the combination of vector similar ity and the m-Simpson coefficient effectively reduces false positives and in creases accuracy. To discuss the choice of vocabulary construction, we pre pared both a sampled vocabulary on an evaluation dataset and a basic pre trained vocabulary on a training dataset. In addition, we tested our method on vocabularies of different sizes. Our experimental results show that the proposed method dramatically improves precision scores especially on the sampled vocabulary and performs better than the state-of-the-art methods that use pre-trained vocabularies. We further introduce a method to deter mine the k value of top-k relevant searches for each image and show that it obtains higher precision at the same recall.
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/EF15_003%2F0000468" target="_blank" >EF15_003/0000468: Intelligent Machine Perception</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEICE Transactions on Information and Systems
ISSN
0916-8532
e-ISSN
1745-1361
Volume of the periodical
E105
Issue of the periodical within the volume
9
Country of publishing house
JP - JAPAN
Number of pages
10
Pages from-to
1590-1599
UT code for WoS article
000852731400009
EID of the result in the Scopus database
2-s2.0-85138462029