Supervised and unsupervised segmentation using superpixels, model estimation, and graph cut

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023001%3A_____%2F17%3A00076325" target="_blank" >RIV/00023001:_____/17:00076325 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21230/17:00314379 RIV/60461373:22340/17:43913234

Result on the web

<a href="https://www.spiedigitallibrary.org/journals/Journal-of-Electronic-Imaging/volume-26/issue-6/061610/Supervised-and-unsupervised-segmentation-using-superpixels-model-estimation-and-graph/10.1117/1.JEI.26.6.061610.short" target="_blank" >https://www.spiedigitallibrary.org/journals/Journal-of-Electronic-Imaging/volume-26/issue-6/061610/Supervised-and-unsupervised-segmentation-using-superpixels-model-estimation-and-graph/10.1117/1.JEI.26.6.061610.short</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1117/1.JEI.26.6.061610" target="_blank" >10.1117/1.JEI.26.6.061610</a>

Result language

angličtina

Original language name

Supervised and unsupervised segmentation using superpixels, model estimation, and graph cut

Original language description

Image segmentation is widely used as an initial phase of many image analysis tasks. It is often advantageous to first group pixels into compact, edge-respecting superpixels, because these reduce the size of the segmentation problem and thus the segmentation time by an order of magnitudes. In addition, features calculated from superpixel regions are more robust than features calculated from fixed pixel neighborhoods. We present a fast and general multiclass image segmentation method consisting of the following steps: (i) computation of superpixels; (ii) extraction of superpixel-based descriptors; (iii) calculating image-based class probabilities in a supervised or unsupervised manner; and (iv) regularized superpixel classification using graph cut. We apply this segmentation pipeline to five real-world medical imaging applications and compare the results with three baseline methods: pixelwise graph cut segmentation, supertexton-based segmentation, and classical superpixel-based segmentation. On all datasets, we outperform the baseline results. We also show that unsupervised segmentation is surprisingly efficient in many situations. Unsupervised segmentation provides similar results to the supervised method but does not require manually annotated training data, which is often expensive to obtain.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

30202 - Endocrinology and metabolism (including diabetes, hormones)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

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

Publication year

2017

Confidentiality

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

Name of the periodical

Journal of electronic imaging

ISSN

1017-9909

e-ISSN

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Volume of the periodical

26

Issue of the periodical within the volume

6

Country of publishing house

US - UNITED STATES

Number of pages

17

Pages from-to

"art. no. 061610"

UT code for WoS article

000419961800012

EID of the result in the Scopus database

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