A comprehensive and systematic review on classical and deep learning based region proposal algorithms. (1st March 2022) - Taghizadeh, Maryam; Chalechale, Abdolah
Abstract: Development of region proposal algorithms has rapidly become one of the most critical research areas over recent years. The perfect accuracy of region-based recognition techniques has led to the use of proposal algorithms as an imperative core in various recognition problems. The main purpose of these algorithms is to extract effective regions of an image with an appropriate number that will reduce the search space and increase detection accuracy. The early development of these algorithms was based on a set of hand-crafted features. Recently, with advances in deep learning techniques, they have been widely and successfully applied to the region proposals. This paper reviews region proposal algorithms, theory, and evaluation metrics and also addresses the existing challenges. In addition, we present a classification for generating proposals, including classical and advanced methods based on hand-crafted features and deep learning, respectively. Both categories are described in details, and an extensive review of recent works is presented. The proposal improvement methods, including ranking algorithms, are also described. In total, more than 60 different algorithms have been studied and classified, and we also point out several applications based on region proposals. Highlights: A comprehensive review of recent works of region proposal algorithms is presented. A taxonomy of region proposals is presented. Advantages and disadvantages of different categories in this area are discussed. Applications in different areas including natural and medical images are reviewed. Different challenges and open-questions are pointed out.