Deep Learning for Scene Classification: A Survey

TL;DR

This survey reviews recent advances in scene classification using deep learning, highlighting datasets, methods, challenges, and future research directions in the field of computer vision.

Contribution

It provides a comprehensive overview of over 200 publications on deep learning-based scene classification, summarizing key achievements and identifying future opportunities.

Findings

Deep learning has significantly improved scene classification accuracy.

Benchmark datasets have facilitated progress and comparison of methods.

The survey highlights challenges and promising research directions.

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.