关键词: 超像素, 超像素分割, 加权核K-means, 密度, 初始中心点

Abstract: Superpixel is a kind of image preprocessing technology which has been developed rapidly in recent years and is widely used in the field of computer vision. Simple linear iterative clustering (SLIC) algorithm is a kind of image preprocessing technology framework. The algorithm achieves a good segmentation result by clustering based on the color and distance characteristics of pixels. However, some problems still exist in the SLIC algorithm. This paper proposes a new SLIC algorithm (WKK-SLIC algorithm) based on optimizing initial center point of weighted kernel K-means clustering. The algorithm is based on the similarity metric that measures the color similarity and space proximity between image pixels, uses the normalization formula of the superpixel segmentation and uses a kernel function to approximate the similarity metric. The algorithm maps the values and coordinates of pixels into the high-dimensional feature space. By assigning an appropriate weight to each pixel in the feature space, the objective function optimization of the weighted K-means and the normalized cuts is mathematically equivalent. Thus the objective function of normalization is optimized by iteratively applying simple K-means clustering in the proposed feature space. The algorithm uses density-sensitive similarity measure to calculate the density of spatial pixels, and heuristically generates the initial center of K-means clustering to achieve stable clustering results. The experimental results show that the algorithm outperforms the SLIC in several commonly used metrics of superpixel segmentation.

Key words: superpixel, superpixel segmentation, weighted kernel K-means, density, initial center point