Abstract: Simple non-iterative clustering (SNIC) algorithm can′t adhere well to object boundaries. To address this drawback, this paper proposes a multi-feature non-iterative superpixels segmentation (MNSS). In feature extraction, Gaussian convolution is used to obtain the horizontal and vertical color gradient features of each pixel in the lab color space; Then the morphological contour features of each pixel are obtained by erosion and dilation operations, which can enhance the edge hit rate of the algorithm without losing the representation of gradient features. And last, based on the non-iterative clustering framework of SNIC algorithm, superpixel segmentation is realized depending on the weighted distance of color, space, color gradient and morphological contour features between pixels. The experimental results on BSDS500 public dataset show that the proposed MNSS algorithm can effectively improve the segmentation accuracy of superpixels while ensuring low time complexity compared with the five mainstream algorithms under the condition of generating the same number of superpixels.