NSLIC: SLIC superpixels based on nonstationarity measure

Superpixels become more and more popular as image preprocessing step in computer vision applications. In this paper, we propose an improved simple linear iterative clustering (SLIC) superpixel approach based on nonstationarity measure (NS-M), which is called nSLIC. An adjustive distance measure is developed in the five-dimensional [labxy] space. The nSLIC superpixel replaces the predefined fixed value of compactness parameter by the nonstationarity measure map of each image, which exploits the image information and is therefore adaptive to the color feature of the image. It also avoids the difficulty of pre-setting compactness parameter and reduces the parameters needed setting to only one indeed. The nSLIC superpixel improves not only segmentation quality bust also computational efficiency by the way of achieving faster convergence. Experiments done on BSD500 dataset show that nSLIC adheres better to image edges meanwhile producing regular and compact superpixels as much as possible, compared to various popular versions of SLIC.