Journal of Advanced Information Technology and Convergence (한국정보기술학회 영문논문지)

Korean Institute of Information Technology (한국정보기술학회)
권호 표지
DOI QR코드

DOI QR Code

Interactive Region Segmentation Method Using Agglomerative Clustering

  • Park, Sanghyun (Dept. of Multimedia Engineering, Sunchon National University)
  • Received : 2018.11.25
  • Accepted : 2018.12.26
  • Published : 2018.12.31
⟨ Previous Next ⟩

Abstract

Due to global warming, various natural disasters such as floods and droughts are increasing. If we can detect the possibility of natural disasters in advance, we can prevent massive damages caused by natural disasters. Recent advances in visual sensor technologies have enabled remote monitoring of a variety of natural environments, including lakes, rivers, and shores. In this paper, we propose a method to segment an image obtained from video sensor networks into regions in order to monitor the environment effectively. In the proposed method, we first partition the image into superpixels and model the connections between superpixels as a graph. Then, initial seeds for each region are set by using the prior information, and the initial seeds are expanded to form regions using agglomerative clustering. Experimental results show that the proposed method extracts the regions from natural environment images easily and accurately.

Keywords

References

  1. Y. Kim, S. Yang, K. Yu, and D. Kim, "Flood Runoff Calculation using Disaster Monitoring CCTV System", Journal of Environmental Science International, Vol. 23, No. 4, pp. 571-584, Apr. 2014. https://doi.org/10.5322/JESI.2014.4.571
  2. G. Xu, W. Shen, and X. Wang, "Applications of wireless sensor networks in marine environment monitoring: A survey", Sensors, Vol. 14, No. 9, pp. 16932-16954, Sep. 2014. https://doi.org/10.3390/s140916932
  3. M. Devlin, C. Petus, E. Silva, D. Tracey, N. Wolff, J. Waterhouse, and J. Brodie, "Water quality and river plume monitoring in the Great Barrier Reef: an overview of methods based on ocean colour satellite data", Remote Sensing, Vol. 7, No. 10, pp. 12909-12941, Sep. 2015. https://doi.org/10.3390/rs71012909
  4. Y. Kim, M. Kim, K. Park, and S. Kim, "A Study on the Implementation of the Wireless Video Security System", Journal of KIIT, Vol. 10, No. 8, pp. 61-68, Aug. 2012.
  5. Z. Pan, L. Chen, and X. Sun, "Low Complexity HEVC Encoder for Visual Sensor Networks", Sensors, Vol. 15, No. 12, pp. 30115-30125, Dec. 2015. https://doi.org/10.3390/s151229788
  6. K. Nam, "A Study on Yeong-san River Ecological Environment Monitoring based on IoT", Journal of The Korea Institute of Electronic Communication Sciences, Vol. 10, No. 2, pp. 203-210, Feb. 2015. https://doi.org/10.13067/JKIECS.2015.10.2.203
  7. J. Paek, J. Hicks, S. Coe, and R. Govindan, "Image-Based Environmental Monitoring Sensor Application Using an Embedded Wireless Sensor Network", Sensors, Vol. 14, No. 9, pp. 15981-16002, Sep. 2014. https://doi.org/10.3390/s140915981
  8. Y. Lee, "The Application of USN for River Measurement: The Yang-Jae Brooket Case", Journal of the Korean Society of Civil Engineers, Vol. 57, No. 5, pp.10-24, May 2009.
  9. M. Sonka, V. Hlavac, and R. Boyle, "Image Processing, Analysis, and Machine Vision", Cengage Learning, Stamford, USA, 2014, pp. 790-794.
  10. M. Kass, A. Witkin, and D. Terzopoulos, "Snakes: Active contour models", International Journal of Computer Vision, Vol. 1, No. 4, pp. 321-331, 1988. https://doi.org/10.1007/BF00133570
  11. R. Szeliski, "Computer Vision: Algorithms and Applications", Springer Science & Business Media, London, UK, 2011, pp. 238-242.
  12. Y. Boykov and M. P. Jolly, "Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images", In Eighth International Conference on Computer Vision, Vancouver, Canada, July 2001, pp. 105-112.
  13. 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
  14. 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
  15. T. Kavzoglu and H. Tonbul, "An experimental comparison of multi-resolution segmentation, SLIC and K-means clustering for object-based classification of VHR imagery", International Journal of Remote Sensing, Vol. 39, No. 18, pp. 6020-6036, Aug. 2018. https://doi.org/10.1080/01431161.2018.1506592