A survey of 2D shape representation: Methods, evaluations, and future research directions

In the past few years, the research studies in image-based shape representation have been proliferating due to its usefulness and importance for various application. This field has been evolved, from simple descriptor-based instance retrieval to utilization of machine learning approaches. Thus, this papers aims to provide a comprehensive survey to summarize the overall view of this research topic. It covers several concepts including the traditional shape descriptors, boundary and region partitioning strategies, and more advanced techniques which commonly exist in the recent studies. This manuscript discusses the advantages and drawbacks of these methods by providing comparisons of evaluation results on well-known public datasets under the various types of similarity metrics and assessment procedures. To complete the survey, it also suggests diverse possibilities of future research directions.