Superpixel methods are image pre-processing technologies that have rapidly developed in recent years. These methods can segment an image into a certain amount of semantically meaningful sub-regions. Compared with the basic element pixels in the traditional image processing methods

superpixels have better abstraction of image local features and better representations of structural information. Furthermore

superpixels can dramatically reduce the complexity of the subsequent processing. Given these significant advantages

superpixels have been widely used in computer vision

particularly in image segmentation. Considering its theoretical value

this study comprehensively reviews the existing superpixel methods and their applications in image segmentation. The history of superpixel segmentation is reviewed

and the superpixel segmentation algorithms are compared in experiments using evaluation metrics to present their performance in superpixel segmentation. Then

the applications of superpixels in image segmentation are categorized and introduced. Finally

the existing limits of the superpixel segmentation algorithms are shown

and the implicit directions for future research on superpixels are concluded. The fundamental concepts

advantages

and disadvantages of the superpixel segmentation algorithms and the applications of superpixels in image segmentation are reviewed. The limits of superpixel segmentation algorithms are presented on the basis of several experiments. As effective pre-processing technologies

superpixel methods have relatively high research value. However

the limitations of superpixels require further research. These limitations include contradictions between the amount of superpixels and the segmentation quality

the superpixel segmentations of some particular objects

and so on.