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Exploring Semi-Supervised Methods for Labeling Support in Multimodal Datasets

Working with multimodal datasets is a challenging task as it requires annotations which often are time consuming and difficult to acquire. This includes in particular video recordings which often need to be watched as a whole before they can be labeled. Additionally, other modalities like accelerati...

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Detalles Bibliográficos
Autores principales: Diete, Alexander, Sztyler, Timo, Stuckenschmidt, Heiner
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6112036/
https://www.ncbi.nlm.nih.gov/pubmed/30103525
http://dx.doi.org/10.3390/s18082639
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author Diete, Alexander
Sztyler, Timo
Stuckenschmidt, Heiner
author_facet Diete, Alexander
Sztyler, Timo
Stuckenschmidt, Heiner
author_sort Diete, Alexander
collection PubMed
description Working with multimodal datasets is a challenging task as it requires annotations which often are time consuming and difficult to acquire. This includes in particular video recordings which often need to be watched as a whole before they can be labeled. Additionally, other modalities like acceleration data are often recorded alongside a video. For that purpose, we created an annotation tool that enables to annotate datasets of video and inertial sensor data. In contrast to most existing approaches, we focus on semi-supervised labeling support to infer labels for the whole dataset. This means, after labeling a small set of instances our system is able to provide labeling recommendations. We aim to rely on the acceleration data of a wrist-worn sensor to support the labeling of a video recording. For that purpose, we apply template matching to identify time intervals of certain activities. We test our approach on three datasets, one containing warehouse picking activities, one consisting of activities of daily living and one about meal preparations. Our results show that the presented method is able to give hints to annotators about possible label candidates.
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spelling pubmed-61120362018-08-30 Exploring Semi-Supervised Methods for Labeling Support in Multimodal Datasets Diete, Alexander Sztyler, Timo Stuckenschmidt, Heiner Sensors (Basel) Article Working with multimodal datasets is a challenging task as it requires annotations which often are time consuming and difficult to acquire. This includes in particular video recordings which often need to be watched as a whole before they can be labeled. Additionally, other modalities like acceleration data are often recorded alongside a video. For that purpose, we created an annotation tool that enables to annotate datasets of video and inertial sensor data. In contrast to most existing approaches, we focus on semi-supervised labeling support to infer labels for the whole dataset. This means, after labeling a small set of instances our system is able to provide labeling recommendations. We aim to rely on the acceleration data of a wrist-worn sensor to support the labeling of a video recording. For that purpose, we apply template matching to identify time intervals of certain activities. We test our approach on three datasets, one containing warehouse picking activities, one consisting of activities of daily living and one about meal preparations. Our results show that the presented method is able to give hints to annotators about possible label candidates. MDPI 2018-08-11 /pmc/articles/PMC6112036/ /pubmed/30103525 http://dx.doi.org/10.3390/s18082639 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Diete, Alexander
Sztyler, Timo
Stuckenschmidt, Heiner
Exploring Semi-Supervised Methods for Labeling Support in Multimodal Datasets
title Exploring Semi-Supervised Methods for Labeling Support in Multimodal Datasets
title_full Exploring Semi-Supervised Methods for Labeling Support in Multimodal Datasets
title_fullStr Exploring Semi-Supervised Methods for Labeling Support in Multimodal Datasets
title_full_unstemmed Exploring Semi-Supervised Methods for Labeling Support in Multimodal Datasets
title_short Exploring Semi-Supervised Methods for Labeling Support in Multimodal Datasets
title_sort exploring semi-supervised methods for labeling support in multimodal datasets
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6112036/
https://www.ncbi.nlm.nih.gov/pubmed/30103525
http://dx.doi.org/10.3390/s18082639
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