  With the rise of deep learning algorithms nowadays, scene image
representation methods have achieved a significant performance boost in
classification. However, the performance is still limited because the scene
images are mostly complex having higher intra-class dissimilarity and
inter-class similarity problems. To deal with such problems, there have been
several methods proposed in the literature with their advantages and
limitations. A detailed study of previous works is necessary to understand
their advantages and disadvantages in image representation and classification
problems. In this paper, we review the existing scene image representation
methods that are being widely used for image classification. For this, we,
first, devise the taxonomy using the seminal existing methods proposed in the
literature to this date {using deep learning (DL)-based, computer vision
(CV)-based, and search engine (SE)-based methods}. Next, we compare their
performance both qualitatively (e.g., quality of outputs, pros/cons, etc.) and
quantitatively (e.g., accuracy). Last, we speculate on the prominent research
directions in scene image representation tasks using {keyword growth and
timeline analysis.} Overall, this survey provides in-depth insights and
applications of recent scene image representation methods under three different
methods.
