Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Portrait segmentation using ensemble of heterogeneous deep-learning models Subject The topic of the resource Deep learning; Efficiency; Ensemble; Portrait segmentation; Simple soft voting; Stacking; Weighted soft voting Description An account of the resource Image segmentation plays a central role in a broad range of applications, such as medical image analysis, autonomous vehicles, video surveillance and augmented reality. Portrait segmenta-tion, which is a subset of semantic image segmentation, is widely used as a preprocessing step in multiple applications such as security systems, entertainment applications, video conferences, etc. A substantial amount of deep learning-based portrait segmentation approaches have been developed, since the performance and accuracy of semantic image segmentation have improved significantly due to the recent introduction of deep learning technology. However, these approaches are limited to a single portrait segmentation model. In this paper, we propose a novel approach using an ensemble method by combining multiple heterogeneous deep-learning based portrait segmentation models to improve the segmentation performance. The Two-Models ensemble and Three-Models ensemble, using a simple soft voting method and weighted soft voting method, were experimented. Intersection over Union (IoU) metric, IoU standard deviation and false prediction rate were used to evaluate the performance. Cost efficiency was calculated to analyze the efficiency of segmentation. The experiment results show that the proposed ensemble approach can perform with higher accuracy and lower errors than single deep-learning-based portrait segmentation models. The results also show that the ensemble of deep-learning models typically increases the use of memory and computing power, although it also shows that the ensemble of deep-learning models can perform more efficiently than a single model with higher accuracy using less memory and less computing power. 2021 by the authors. Licensee MDPI, Basel, Switzerland. Creator An entity primarily responsible for making the resource Kim Y.-W.; Byun Y.-C.; Krishna A.V.N. Source A related resource from which the described resource is derived Entropy, Vol-23, No. 2, pp. 1-20. Publisher An entity responsible for making the resource available Multidisciplinary Digital Publishing Institute (MDPI) Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.3390/e23020197" target="_blank" rel="noreferrer noopener">https://doi.org/10.3390/e23020197</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100772450&amp;doi=10.3390%2Fe23020197&amp;partnerID=40&amp;md5=4887ebabf7b545a793c6d3aa95b20db8" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100772450&amp;doi=10.3390%2fe23020197&amp;partnerID=40&amp;md5=4887ebabf7b545a793c6d3aa95b20db8</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Relation A related resource ISSN: 10994300 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kim Y.-W., Centre for Digital Innovation, CHRIST (Deemed to be University), Bangalore, Karnataka, 560029, India; Byun Y.-C., Department of Computer Engineering, Jeju National University, 102 Jejudaehak-ro, Jeju-si, 63243, South Korea; Krishna A.V.N., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, Karnataka, 560029, India