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In this paper, we present a fast and effective method of image segmentation. Our design follows the bottom-up approach: first, the image is decomposed by nonparametric clustering; then, similar classes are joined by a merging algorithm that uses color, and adjacency information to obtain consistent image content. The core of the segmenter is a parallel version of the mean shift algorithm that works simultaneously on multiple feature space kernels. Our system was implemented on a many-core GPGPUdoi:10.1186/1687-6180-2011-111 fatcat:latj2lc2g5fwvki4fvl7u7zy3e