Semisupervised Segmentation of UHD Video

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F18%3A00494104" target="_blank" >RIV/67985807:_____/18:00494104 - isvavai.cz</a>

Výsledek na webu

<a href="http://ceur-ws.org/Vol-2203/100.pdf" target="_blank" >http://ceur-ws.org/Vol-2203/100.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Semisupervised Segmentation of UHD Video

Popis výsledku v původním jazyce

One of the key preprocessing tasks in information retrieveal from video is the segmentation of the scene, primarily its segmentation into foreground objects and the background. This is actually a classification task, but with the specific property that it is very time consuming and costly to obtain human-labelled training data for classifier training. That suggests to use semisupervised classifiers to this end. The presented work in progress reports the investigation of semisupervised classification methods based on cluster regularization and on fuzzy c-means in connection with the foreground / background segmentation task. To classify as many video frames as possible using only a single human-based frame, the semisupervised classification is combined with a frequently used keypoint detector based on a combination of a corner detection method with a visual descriptor method. The paper experimentally compares both methods, and for the first of them, also classifiers with different delays between the human-labelled video frame and classifier training.

Název v anglickém jazyce

Semisupervised Segmentation of UHD Video

Popis výsledku anglicky

One of the key preprocessing tasks in information retrieveal from video is the segmentation of the scene, primarily its segmentation into foreground objects and the background. This is actually a classification task, but with the specific property that it is very time consuming and costly to obtain human-labelled training data for classifier training. That suggests to use semisupervised classifiers to this end. The presented work in progress reports the investigation of semisupervised classification methods based on cluster regularization and on fuzzy c-means in connection with the foreground / background segmentation task. To classify as many video frames as possible using only a single human-based frame, the semisupervised classification is combined with a frequently used keypoint detector based on a combination of a corner detection method with a visual descriptor method. The paper experimentally compares both methods, and for the first of them, also classifiers with different delays between the human-labelled video frame and classifier training.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

<a href="/cs/project/GA18-18080S" target="_blank" >GA18-18080S: Objevování znalostí v datech o aktivitě člověka založené na fúzi</a><br>

Návaznosti

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

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

ITAT 2018: Information Technologies – Applications and Theory. Proceedings of the 18th conference ITAT 2018

ISBN

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ISSN

1613-0073

e-ISSN

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Počet stran výsledku

8

Strana od-do

100-107

Název nakladatele

Technical University & CreateSpace Independent Publishing Platform

Místo vydání

Aachen

Místo konání akce

Plejsy

Datum konání akce

21. 9. 2018

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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