Deep Learning for Scene Recognition from Visual Data: A Survey

Alina Matei, Andreea Glavan, Estefanía Talavera*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

11 Citations (Scopus)
37 Downloads (Pure)


The use of deep learning techniques has exploded during the last few years, resulting in a direct contribution to the field of artificial intelligence. This work aims to be a review of the state-of-the-art in scene recognition with deep learning models from visual data. Scene recognition is still an emerging field in computer vision, which has been addressed from a single image and dynamic image perspective. We first give an overview of available datasets for image and video scene recognition. Later, we describe ensemble techniques introduced by research papers in the field. Finally, we give some remarks on our findings and discuss what we consider challenges in the field and future lines of research. This paper aims to be a future guide for model selection for the task of scene recognition.

Original languageEnglish
Title of host publicationHybrid Artificial Intelligent Systems
Subtitle of host publication15th International Conference, HAIS 2020, Gijón, Spain, November 11-13, 2020, Proceedings
EditorsEnrique Antonio de la Cal, José Ramón Villar Flecha, Héctor Quintián, Emilio Corchado
Place of PublicationCham
Number of pages11
ISBN (Electronic)978-3-030-61705-9
ISBN (Print)978-3-030-61704-2
Publication statusPublished - 2020
Externally publishedYes
Event15th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2020 - Gijón, Spain
Duration: 11 Nov 202013 Nov 2020

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2020


  • Computer vision
  • Deep learning
  • Ensemble techniques
  • Scene recognition


Dive into the research topics of 'Deep Learning for Scene Recognition from Visual Data: A Survey'. Together they form a unique fingerprint.

Cite this