Image segmentation using linked mean-shift vectors and its implementation on GPU
SCIE
SCOPUS
- Title
- Image segmentation using linked mean-shift vectors and its implementation on GPU
- Authors
- Hanjoo Cho,; Suk-Ju Kang; Sung In Cho; Kim, YH
- Date Issued
- 2014-11
- Publisher
- IEEE Transactions on Consumer Electronics
- Abstract
- 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).
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/26707
- DOI
- 10.1109/TCE.2014.7027348
- ISSN
- 0098-3063
- Article Type
- Article
- Citation
- IEEE Transactions on Consumer Electronics, vol. 60, no. 4, page. 719 - 727, 2014-11
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