Mean-shift clustering for interactive multispectral image analysis

Mean shift clustering and its recent variants are a viable and popular image segmentation tool. In this paper we investigate mean shift segmentation on multispectral and hyperspectral images and propose three new algorithms. First, we improve segmentation performance by running mean shift on the spectral gradient. At the same time, we adapt a popular superpixel segmentation method to the multispectral domain using modified similarity measures from spectral mapping. Based on superpixels, we design two mean shift variants that both obtain competitive segmentation results in significantly reduced running time. For one variant, the speedup in our benchmark is over 100 times. This enables mean shift clustering in an interactive setting.