Selecting Image Pairs for SfM by Introducing Jaccard Similarity

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F17%3A00329401" target="_blank" >RIV/68407700:21730/17:00329401 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/7986764" target="_blank" >https://ieeexplore.ieee.org/document/7986764</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23919/MVA.2017.7986764" target="_blank" >10.23919/MVA.2017.7986764</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Selecting Image Pairs for SfM by Introducing Jaccard Similarity

Popis výsledku v původním jazyce

We present a new approach for selecting image pairs that are more likely to match in Structure from Motion (SfM). We propose to use Jaccard Similarity (JacS) which shows how many different visual words is shared by an image pair. In our method, the similarity between images is evaluated using JacS of bag-of-visual-words in addition to tf-idf, which is popular for this purpose. To evaluate the efficiency of our method, we carry out experiments on our original datasets as well as on Pantheon dataset, which is derived from Flickr. The result of our method using both JacS and tf-idf is better than the results of a standard method using tf-idf only.

Název v anglickém jazyce

Selecting Image Pairs for SfM by Introducing Jaccard Similarity

Popis výsledku anglicky

We present a new approach for selecting image pairs that are more likely to match in Structure from Motion (SfM). We propose to use Jaccard Similarity (JacS) which shows how many different visual words is shared by an image pair. In our method, the similarity between images is evaluated using JacS of bag-of-visual-words in addition to tf-idf, which is popular for this purpose. To evaluate the efficiency of our method, we carry out experiments on our original datasets as well as on Pantheon dataset, which is derived from Flickr. The result of our method using both JacS and tf-idf is better than the results of a standard method using tf-idf only.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

R - Projekt Ramcoveho programu EK

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

Machine Vision Applications (MVA), 2017 15th IAPR International Conference on

ISBN

978-4-901122-16-0

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

25-29

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Nagoya

Datum konání akce

8. 5. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000426950300007