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 Conference Papers Conference Paper Faculty Publications- Conference Papers 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 Accuracy Enhancement of Portrait Segmentation by Ensembling Deep Learning Models Subject The topic of the resource Deep Learning; Ensemble Method; Portrait Segmentation; Simple Soft Voting; Weighted Soft Voting Description An account of the resource Portrait segmentation is widely used as a preprocessing step in multiple applications. The accuracy of portrait segmentation models indicates its reliability. In recent times, portrait segmentation using deep learning models have achieved significant success in performance and accuracy. However, these portrait segmentation models are limited to a single model. In this paper, we propose ensemble approach using multiple portrait segmentation models to improve the segmentation accuracy. The result of experiment shows that the proposed ensemble approach produces better accuracy than individual models. Accuracy of single models and proposed ensemble approach were compared with Intersection over Union (IoU) metric and false prediction rate to evaluate the accuracy performance. The result shows reduced false negative rate and false discovery rate, this reduction in false prediction has enabled ensemble approach to produce segmented images with optimized error and improved result of segmentation in portrait area of human body than individual portrait segmentation models 2020 IEEE. Creator An entity primarily responsible for making the resource Kim Y.W.; Innila Rose J.; Krishna A.V.N. Source A related resource from which the described resource is derived Proceedings - 2020 5th International Conference on Research in Computational Intelligence and Communication Networks, ICRCICN 2020, pp. 59-64. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2020-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICRCICN50933.2020.9296196" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICRCICN50933.2020.9296196</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099198426&amp;doi=10.1109%2FICRCICN50933.2020.9296196&amp;partnerID=40&amp;md5=6f0718be72d3348bb71125fb8f9dfc12" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099198426&amp;doi=10.1109%2fICRCICN50933.2020.9296196&amp;partnerID=40&amp;md5=6f0718be72d3348bb71125fb8f9dfc12</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-172818818-8 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 Conference paper 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., CHRIST (Deemed to Be University), Centre for Digital Innovation, Bangalore, India; Innila Rose J., CHRIST (Deemed to Be University), Centre for Digital Innovation, Bangalore, India; Krishna A.V.N., CHRIST (Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India