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Image Segmentation Using Linked Mean-Shift Vectors and Its Implementation on GPU
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DOI：10.1109/TCE.2014.7027348.png)
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Abstract
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This paper proposes a new approach to mean-shift-based image segmentation that uses a non-iterative process to determine the maxima of the underlying density, which are called modes. To identify the mode, the proposed approach performs a mean-shift process on each pixel only once, and uses the resulting mean-shift vectors to construct links for the pairs of pixels, instead of iteratively performing the mean-shift process. Then, it groups the pixels of the same mode, connected through the links, into the same cluster. Although the proposed approach performs the mean-shift process only once, it provides comparable segmentation quality to the conventional approaches. In experiments using benchmark images, the processing time was reduced to a quarter, while probabilistic rand index and segmentation covering were well maintained; they were degraded by only 0.38% and 1.87%, respectively. Furthermore, the proposed algorithm improves the locality of the required data and compute-intensity of the algorithm, which are important factors for utilizing the GPU effectively. The proposed algorithm, when implemented on a GPU, improved the processing speed by over 75 times compared to implementation on a CPU, while the conventional approach was accelerated by about 15 times(1).
Keywords:
Mean-shift algorithm
parallel processing
image segmentation
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