한국정보통신학회논문지 (Journal of the Korea Institute of Information and Communication Engineering)

  • 제23권12호
  • /
  • Pages.1520-1527
  • /
  • 2019
  • /
  • 2234-4772(pISSN)
  • /
  • 2288-4165(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
권호 표지
DOI QR코드

DOI QR Code

효율적인 그래프 기반 2단계 슈퍼픽셀 생성 방법

Efficient graph-based two-stage superpixel generation method

  • Park, Sanghyun (Department of Multimedia Engineering, Sunchon National University)
  • 투고 : 2019.08.01
  • 심사 : 2019.08.18
  • 발행 : 2019.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

컴퓨터 비전 분야에서 영상의 특성을 유지하면서 영상을 간소화하여 계산량을 줄이는 방법으로 전처리 단계에서 슈퍼픽셀 방법이 많이 사용되고 있다. 하지만 슈퍼픽셀 단계에서는 영상의 특성을 고려하는 것 보다는 화소의 값을 기준으로 일정한 크기와 형태의 슈퍼픽셀을 생성하는 것이 일반적이다. 본 논문에서는 응용에 맞게 영상의 특성을 고려하여 슈퍼픽셀을 생성할 수 있는 방법을 제안한다. 제안하는 방법은 두 단계로 이루어지며, 첫 번째 단계에서 영상을 과분할 하여 영상의 경계 정보들이 잘 보존되게 한다. 두 번째 단계에서는 과분할 된 슈퍼픽셀들을 유사도를 기준으로 병합하여 원하는 개수의 슈퍼픽셀을 생성한다. 이때 슈퍼픽셀의 최대 크기를 제한함으로써 슈퍼픽셀의 형태를 제어한다. 실험 결과는 제안하는 방법으로 생성된 슈퍼픽셀이 기존 방법에 의해 생성된 슈퍼픽셀 보다 정확하게 경계 정보를 보존하는 것을 보여준다.

Superpixel methods are widely used in the preprocessing stage as a method to reduce computational complexity by simplifying images while maintaining the characteristics of images in the field of computer vision. It is common to generate superpixels with a regular size and form based on the pixel values rather than considering the characteristics of the image. In this paper, we propose a method to generate superpixels considering the characteristics of an image according to the application. The proposed method consists of two steps, and the first step is to oversegment an image so that the boundary information of the image is well preserved. In the second step, superpixels are merged based on similarity to produce the desired number of superpixels, where the form of superpixels are controlled by limiting the maximum size of superpixels. Experimental results show that the proposed method preserves the boundaries of an image more accurately than the existing method.

키워드

참고문헌

  1. J. Kim, "The Object Image Detection Method using statistical properties," Journal of the Korea Institute of Information and Communication Engineering, vol. 22. no. 7, pp. 956-962, Jul. 2018. https://doi.org/10.6109/JKIICE.2018.22.7.956
  2. J. Yao, M. Boben, S. Fidler, and R. Urtasun, "Real-time coarse-to-fine topologically preserving segmentation," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston:MA, pp. 2947-2955, 2015.
  3. R. Giraud, V. T. Ta, and N. Papadakis. "Evaluation framework of superpixel methods with a global regularity measure," Journal of Electronic Imaging, vol. 26, no. 6, 2017.
  4. D. Stutz, A. Hermans, and B. Leibe. "Superpixels: An evaluation of the state-of-the-art," Computer Vision and Image Understanding, vol. 166, pp. 1-27, Jan. 2018. https://doi.org/10.1016/j.cviu.2017.03.007
  5. A. Rubio, L. Yu, E. Simo-Serra, and F. Moreno-Noguer, "BASS: boundary-aware superpixel segmentation," In Proceeding of the International Conference on Pattern Recognition, Cancun, Mexico, pp. 2824-2829, 2016.
  6. M. Reso, J. Jachalsky, B. Rosenhahn, and J. Ostermann, "Temporally consistent superpixels," in Proceedings of the IEEE International Conference on Computer Vision, Sydney: Australia, pp. 385-392, 2013.
  7. J. Shi and J. Malik. "Normalized cuts and image segmentation," IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888-905, Aug. 2000. https://doi.org/10.1109/34.868688
  8. P. F. Felzenszwalb and D. P. 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
  9. L. Vincent and P. Soille. "Watersheds in digital spaces: an efficient algorithm based on immersion simulations," IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 13, no. 6, pp. 583-598, June 1991. https://doi.org/10.1109/34.87344
  10. D. Comaniciu and M. Peter, "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
  11. R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, "Slic superpixels compared to state-of-the-art superpixel methods," IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 34, no. 11, pp. 2274-2282, Nov. 2012. https://doi.org/10.1109/TPAMI.2012.120
  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. M. Van den Bergh, X. Boix, G. Roig, and L. Van Gool, "Seeds: Superpixels extracted via energy-driven sampling," International Journal of Computer Vision, vol. 111, no. 3, pp. 298-314, Feb. 2015. https://doi.org/10.1007/s11263-014-0744-2
  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, 2001.