Exploring Semi-Supervised Methods for Labeling Support in Multimodal Datasets
AbstractWorking 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. View Full-Text
Share & Cite This Article
Diete, A.; Sztyler, T.; Stuckenschmidt, H. Exploring Semi-Supervised Methods for Labeling Support in Multimodal Datasets. Sensors 2018, 18, 2639.
Diete A, Sztyler T, Stuckenschmidt H. Exploring Semi-Supervised Methods for Labeling Support in Multimodal Datasets. Sensors. 2018; 18(8):2639.Chicago/Turabian Style
Diete, Alexander; Sztyler, Timo; Stuckenschmidt, Heiner. 2018. "Exploring Semi-Supervised Methods for Labeling Support in Multimodal Datasets." Sensors 18, no. 8: 2639.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.