Review on superpixel methods in image segmentation
Objective 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.Method 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.Result 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.Conclusion 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.
NETL
NSTL
万方数据
维普
