From Global to Hybrid: A Review of Supervised Deep Learning for 2-D Image Feature Representation

Abstract

Computer vision is the science that aims to enable computers to emulate human visual perception, and it encompasses various techniques and methods for extracting and interpreting information from two-dimensional images. Supervised deep 2-D image feature representation is a fundamental problem in computer vision that applies deep learning techniques to extract and process information from a given 2-D image under supervised settings. The goal is to obtain a feature vector that can be utilized for various downstream computer vision applications. The quality of supervised deep 2-D image feature representation algorithms directly affects the performance of downstream applications. However, most of the existing vision research only explores supervised deep 2-D image feature representation for specific subtasks. Therefore, a comprehensive discussion on this topic is needed. In this article, we propose a taxonomy of supervised deep 2-D image feature representation methods based on four categories: global representation, region representation, hash representation, and hybrid representation, and we introduce their typical approaches. Furthermore, we perform a comparative analysis of the representative methods on three fundamental tasks: image classification, object detection, and semantic segmentation, as well as other common tasks. We also discuss the limitations of supervised deep 2-D image feature representation and investigate future directions in image representation to facilitate the advancement of computer vision through image representation.