The Journal of Korean Institute of Information Technology (한국정보기술학회논문지)

Korean Institute of Information Technology (한국정보기술학회)
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Superpixel Segmentation Scheme Using Image Complexity

영상의 복잡도를 고려한 슈퍼픽셀 분할 방법

  • Park, Sanghyun (Dept. of Multimedia Engineering, Sunchon National University)
  • 박상현 (순천대학교 멀티미디어공학과)
  • Received : 2018.09.17
  • Accepted : 2018.10.27
  • Published : 2018.12.31
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Abstract

When using complicated image processing algorithms, we use superpixels to reduce computational complexity. Superpixel segmentation is a method of grouping pixels having similar characteristics into one group. Since superpixel is used as a preprocessing of image processing, it should be generated quickly, and the edge components of the image should be well preserved. In this paper, we propose a method of generating superpixels with a small amount of computation while preserving edge components well. In the proposed method, superpixels of an image are generated by using the existing k-mean method, and similar superpixels among the generated superpixels are merged to make final superpixels. When merging superpixels, the similarity is calculated only for superpixels. Therefore, the amount of computation is maintained small. It is shown by experimental results that the superpixel images produced by the proposed method are conserving edge information of the original image better than those produced by the existing method.

복잡한 영상처리 알고리즘을 사용할 때 계산량을 줄이기 위해 슈퍼픽셀을 사용한다. 슈퍼픽셀은 특성이 유사한 픽셀들을 군집화하여 하나의 그룹으로 만드는 방법이다. 슈퍼픽셀은 영상처리의 전단계로 사용되기 때문에 빠르게 생성할 수 있어야 하고 영상의 에지 성분들을 잘 보존하여야 한다. 본 논문에서는 에지 성분을 잘 보존하면서도 계산량이 많지 않은 슈퍼픽셀 생성 방법을 제안한다. 제안하는 방법에서는 먼저 기존의 k-mean 방법을 이용하여 영상의 슈퍼픽셀을 충분히 생성하고, 생성된 슈퍼픽셀들을 분석하여 유사한 슈퍼픽셀을 병합하는 방식으로 최종 슈퍼픽셀을 생성한다. 슈퍼픽셀을 병합할 때는 슈퍼픽셀에 대해서만 유사도를 측정하기 때문에 추가되는 계산량은 많지 않다. 실험 결과는 제안하는 방법으로 생성된 슈퍼픽셀이 기존 방법에 의해 생성된 슈퍼픽셀에 비해 보다 정확하게 에지 성분들을 보존하는 것을 보여준다.

Keywords

Acknowledgement

Supported by : 순천대학교

References

  1. S. Kim and B. Park, "Image segmentation algorithm based on weight information", Journal of KIIT, Vol. 9, No. 5, pp. 472-477, May 2016.
  2. Z. Liu, W. Zou, and O. Meur, "Saliency tree: A novel saliency detection framework", IEEE Transactions on Image Processing, Vol. 23, No. 5, pp. 1937-1952, May 2014. https://doi.org/10.1109/TIP.2014.2307434
  3. R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, "SLIC superpixels compared to state-of-the-art superpixel methods", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34, No. 11, pp. 2274-2282, Nov. 2012. https://doi.org/10.1109/TPAMI.2012.120
  4. Y. Li, Y. Tan, J. Yu, S. Qi, and J. Tian, "Kernel regression in mixed feature spaces for spatio-temporal saliency detection", Computer Vision and Image Understanding, Vol. 135, pp. 126-140, Jun. 2015. https://doi.org/10.1016/j.cviu.2015.01.011
  5. S. Kang, N. Kang, D. Kim, and S. Ko, "A novel depth image enhancement method based on the linear surface model", IEEE Transactions on Consummer Electronics, Vol. 60, No. 4, pp. 710-718, Nov. 2014. https://doi.org/10.1109/TCE.2014.7027347
  6. D. Comaniciu and P. Meer, "Mean shift: A robust approach toward feature space analysis", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 5, pp. 603-619, May 2002. https://doi.org/10.1109/34.1000236
  7. P. Felzenszwalb and D. Huttenlocher, "Efficient graph-based image segmentation", International Journal of Computer Vision, Vol. 59, No. 2, pp. 167-181, Sep. 2004. https://doi.org/10.1023/B:VISI.0000022288.19776.77
  8. A. Levinshtein, A. Stere, K. Kutulakos, D. Fleet, S. Dickinson, and K. Siddiqi, "Turbopixels: fast superpixels using geometric flows", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31, No. 12, pp. 2290-2297, Dec. 2009. https://doi.org/10.1109/TPAMI.2009.96
  9. Z. Li and J. Chen, "Superpixel segmentation using linear spectral clustering", in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, pp. 1356-1363, Jun. 2015.
  10. Y. Zhang, X. Li, X. Gao, and C. Zhang, "A simple algorithm of superpixel segmentation with boundary constraint", IEEE Transaction on Circuits and Systems for Video Technology, Vol. 27, pp. 1502-1514, 2017.
  11. A. Rubio, L. Yu, E. Simo-Serra, and F. Moreno-Noguer, "BASS: boundary-aware superpixel segmentation", in Proceedings of International Conference on Pattern Recognition, Cancun, Mexico, pp. 2824-2829, 2016.
  12. K. Choi and K. Oh, "Subsampling-based acceleration of simple linear iterative clustering for superpixel segmentation", Computer Vision and Image Understanding, Vol. 146, pp. 1-8, May 2016. https://doi.org/10.1016/j.cviu.2016.02.018
  13. R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, S. Susstrunk, "SLIC superpixels", http://ivrl.epfl.ch/research/superpixels, [accessed May 14. 2018]
  14. D. Martin, C. Fowlkes, D. Tal, and J. Malik, "A database of human segmented natural images and its application to evaluating segmentation algorithm and measuring ecological statistics", in Proceedings of IEEE International Conference Computer Vision, Vancouver, Canada, pp. 416-423, Jul. 2001.

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