A survey on deep learning techniques for image and video semantic segmentation

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Title: A survey on deep learning techniques for image and video semantic segmentation
Authors: Garcia-Garcia, Alberto | Orts-Escolano, Sergio | Oprea, Sergiu | Villena Martínez, Víctor | Martínez González, Pablo | Garcia-Rodriguez, Jose
Research Group/s: Robótica y Visión Tridimensional (RoViT) | Informática Industrial y Redes de Computadores
Center, Department or Service: Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial | Universidad de Alicante. Departamento de Tecnología Informática y Computación
Keywords: Semantic segmentation | Deep learning | Scene labeling
Knowledge Area: Ciencia de la Computación e Inteligencia Artificial | Arquitectura y Tecnología de Computadores
Issue Date: Sep-2018
Publisher: Elsevier
Citation: Applied Soft Computing. 2018, 70: 41-65. doi:10.1016/j.asoc.2018.05.018
Abstract: Image semantic segmentation is more and more being of interest for computer vision and machine learning researchers. Many applications on the rise need accurate and efficient segmentation mechanisms: autonomous driving, indoor navigation, and even virtual or augmented reality systems to name a few. This demand coincides with the rise of deep learning approaches in almost every field or application target related to computer vision, including semantic segmentation or scene understanding. This paper provides a review on deep learning methods for semantic segmentation applied to various application areas. Firstly, we formulate the semantic segmentation problem and define the terminology of this field as well as interesting background concepts. Next, the main datasets and challenges are exposed to help researchers decide which are the ones that best suit their needs and goals. Then, existing methods are reviewed, highlighting their contributions and their significance in the field. We also devote a part of the paper to review common loss functions and error metrics for this problem. Finally, quantitative results are given for the described methods and the datasets in which they were evaluated, following up with a discussion of the results. At last, we point out a set of promising future works and draw our own conclusions about the state of the art of semantic segmentation using deep learning techniques.
Sponsor: This work has been funded by the Spanish Government TIN2016-76515-R funding for the COMBAHO project, supported with Feder funds. It has also been supported by a Spanish national grant for PhD studies FPU15/04516 (Alberto Garcia-Garcia). In addition, it was also funded by the grant Ayudas para Estudios de Master e Iniciacion a la Investigacion from the University of Alicante.
URI: http://hdl.handle.net/10045/75753
ISSN: 1568-4946 (Print) | 1872-9681 (Online)
DOI: 10.1016/j.asoc.2018.05.018
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2018 Elsevier B.V.
Peer Review: si
Publisher version: https://doi.org/10.1016/j.asoc.2018.05.018
Appears in Collections:INV - I2RC - Artículos de Revistas
INV - RoViT - Artículos de Revistas

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