With the rise of deep learning algorithms nowadays, scene image
representation methods on big data (e.g., SUN-397) have achieved a significant
performance boost in classification. However, the performance is still limited
because the scene images are mostly complex in nature having higher intra-class
dissimilarity and inter-class similarity problems. To deal with such problems,
there are several methods proposed in the literature with their own advantages
and limitations. A detailed study of previous works is necessary to understand
their pros and cons in image representation and classification. In this paper,
we review the existing scene image representation methods that are being used
widely for image classification. For this, we, first, devise the taxonomy using
the seminal existing methods proposed in the literature to this date. Next, we
compare their performance both qualitatively (e.g., quality of outputs,
pros/cons, etc.) and quantitatively (e.g., accuracy). Last, we speculate the
prominent research directions in scene image representation tasks. Overall,
this survey provides in-depth insights and applications of recent scene image
representation methods for traditional Computer Vision (CV)-based methods, Deep
Learning (DL)-based methods, and Search Engine (SE)-based methods.Comment: This paper is under review in Computer Science Review (Elsevier)
journal. This article may be deleted or updated based on the polices of the
journa