Multiscale Representation Learning for Image Classification: A Survey

Feature representation has been widely used and developed recently. Multiscale features have led to remarkable breakthroughs for representation learning process in many computer vision tasks. This paper aims to provide a comprehensive survey of the recent multiscale representation learning achievements in classification tasks. Multiscale representation learning methods can be divided into two broad categories (multiscale geometric analysis and multiscale networks). Eleven kinds of multiscale geometric tools and seven kinds of multiscale networks are introduced. Some corresponding fundamental subproblems of these two broad categories are also described, including some concepts in representation process, specific representation methods with multiscale geometric analysis, and multiscale representation design strategies for networks. Then, the correlation between these two broad categories is illustrated, including respective characteristics, combination strategies, and characteristics of optimal representation. Some datasets and evaluation results are included to verify the effectiveness of the multiscale representation learning. Eventually, conclusion and future work are given, covering four directions [a) Choice and fusion; b) Self-adaption; c) Structure; and d) Generalization and proof].