Deep Learning for Scene Classification: A Survey
- URL: http://arxiv.org/abs/2101.10531v2
- Date: Sat, 20 Feb 2021 04:39:10 GMT
- Title: Deep Learning for Scene Classification: A Survey
- Authors: Delu Zeng, Minyu Liao, Mohammad Tavakolian, Yulan Guo, Bolei Zhou,
Dewen Hu, Matti Pietik\"ainen, Li Liu
- Abstract summary: Scene classification is a longstanding, fundamental and challenging problem in computer vision.
The rise of large-scale datasets and the renaissance of deep learning techniques have brought remarkable progress in the field of scene representation and classification.
This paper provides a comprehensive survey of recent achievements in scene classification using deep learning.
- Score: 48.57123373347695
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Scene classification, aiming at classifying a scene image to one of the
predefined scene categories by comprehending the entire image, is a
longstanding, fundamental and challenging problem in computer vision. The rise
of large-scale datasets, which constitute the corresponding dense sampling of
diverse real-world scenes, and the renaissance of deep learning techniques,
which learn powerful feature representations directly from big raw data, have
been bringing remarkable progress in the field of scene representation and
classification. To help researchers master needed advances in this field, the
goal of this paper is to provide a comprehensive survey of recent achievements
in scene classification using deep learning. More than 200 major publications
are included in this survey covering different aspects of scene classification,
including challenges, benchmark datasets, taxonomy, and quantitative
performance comparisons of the reviewed methods. In retrospect of what has been
achieved so far, this paper is also concluded with a list of promising research
opportunities.
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