Portrait segmentation using ensemble of heterogeneous deep-learning models

Title

Portrait segmentation using ensemble of heterogeneous deep-learning models

Creator

Kim Y.-W.; Byun Y.-C.; Krishna A.V.N.

Description

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.

Source

Entropy, Vol-23, No. 2, pp. 1-20.

Date

2021-01-01

Publisher

Multidisciplinary Digital Publishing Institute (MDPI)

Subject

Deep learning; Efficiency; Ensemble; Portrait segmentation; Simple soft voting; Stacking; Weighted soft voting

Coverage

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

Rights

All Open Access; Gold Open Access; Green Open Access

Relation

ISSN: 10994300

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.3390/e23020197

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100772450&doi=10.3390%2fe23020197&partnerID=40&md5=4887ebabf7b545a793c6d3aa95b20db8