Superpixels Generating from the Pixel-based K-Means Clustering

Image segmentation is a basic but important preprocessing to image recognition in computer vision applications. In this paper, we propose a pixel-based k-means (PKM) clustering to generate superpixels, which comprise many pixels with similar colors and neighbor positions. In contrast with conventional center-based clustering, the PKM method traces several nearer clustering centers for a pixel in advance, and then the pixel find the highest similar colors as its clustering center. Besides, we adopt the regional clustering of the SLIC (Simple Linear Iterative Clustering) in the PKM method to improve the performance of image segmentations. The MSRC dataset is used to quantitatively compare the PKM with the SLIC performances, such as under-segmentation errors, boundary recall, detection precision, and computation efficiency.