KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
권호 표지
DOI QR코드

DOI QR Code

A New Connected Coherence Tree Algorithm For Image Segmentation

  • Zhou, Jingbo (The School of Computer Science and Technology, Nanjing University of Science and Technology) ;
  • Gao, Shangbing (The School of Computer Science and Technology, Nanjing University of Science and Technology) ;
  • Jin, Zhong (The School of Computer Science and Technology, Nanjing University of Science and Technology)
  • Received : 2011.01.18
  • Accepted : 2012.04.07
  • Published : 2012.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we propose a new multi-scale connected coherence tree algorithm (MCCTA) by improving the connected coherence tree algorithm (CCTA). In contrast to many multi-scale image processing algorithms, MCCTA works on multiple scales space of an image and can adaptively change the parameters to capture the coarse and fine level details. Furthermore, we design a Multi-scale Connected Coherence Tree algorithm plus Spectral graph partitioning (MCCTSGP) by combining MCCTA and Spectral graph partitioning in to a new framework. Specifically, the graph nodes are the regions produced by CCTA and the image pixels, and the weights are the affinities between nodes. Then we run a spectral graph partitioning algorithm to partition on the graph which can consider the information both from pixels and regions to improve the quality of segments for providing image segmentation. The experimental results on Berkeley image database demonstrate the accuracy of our algorithm as compared to existing popular methods.

Keywords

References

  1. Y. Zhang, "Advances in Image and Video Segmentation," IRM Press, 2006.
  2. A. Sleit et al, "Image clustering using color, texture and shape features," KSII Transaction on internet and information system, vol.5, no.1, pp.211-227, 2011.
  3. H.D. Cheng, X.H. Jiang, Y. Sun and J.L. Wang, "Color image segmentation: advances and prospects," Pattern Recognition, vol.34, pp.2259-2281, 2001. https://doi.org/10.1016/S0031-3203(00)00149-7
  4. D.A. Forsyth and J. Ponce, "Computer Vision: A Modern Approach," Prentice-Hall, Englewood Cliffs, 2002.
  5. N. Bonnet, J. Cutrona and M. Herbin, "A „no-threshold‟ histogram-based image segmentation method," Pattern Recognition, vol.35, no.10, pp.2319-2322, 2002. https://doi.org/10.1016/S0031-3203(02)00057-2
  6. E. Navon, O. Miller and A. Averbuch, "Color image segmentation based on adaptive local thresholds," Image and Vision Computing ,vol.23 ,no.1, pp.69-85, 2005. https://doi.org/10.1016/j.imavis.2004.05.011
  7. J. Shi and J. Malik, "Normalized cuts and image segmentation," IEEE Trans. on PAMI, vol.22, no.8, pp.888-905, 2000. https://doi.org/10.1109/34.868688
  8. Y.Y.Boykov and G. Funka-Lea, "Graph cuts and efficient n-d image segmentation," International Journal of Computer Vision, vol.70, no.2, pp.109-131, 2006. https://doi.org/10.1007/s11263-006-7934-5
  9. C. Fowlkes, D. Martin and J. Malik, "Learning affinity funcions for image segmentation: combining patch-based and gradient-based approaches," In Proc. CVPR 2003, vol.2 , pp.54-61, 2003.
  10. S. X. Yu, "Segmentation using multiscale cues," In Proc. CVPR 2004, pp.247-254, 2004.
  11. T. Cour, F. Benezit and J. Shi, "Spectral segmentation with multiscale graph decomposition," In Proc. CVPR 2005, vol.2, pp.1124-1132, 2005.
  12. X. S. Hua, C. Zhang and L. Quan, "Normalized tree partitioning for image segmentation," In Proc. CVPR 2008, pp.1-8, 2008.
  13. S.R.Rao, H.Mobahi, A. Y. Yang, S. S. Sastry and Y. Ma, "Natural image segmentation with adaptive texture and boundary encoding," In Proc. ACCV 2009, pp.135-146, 2009.
  14. Ding J, Ma R and Chen S, "A Scale-based coherence connected tree algorithm for image segmentation," IEEE Trans. on Image Proc., vol.17, no.2, pp.204−216, 2008.
  15. A. Ng, M. Jordan and Y. Weiss, "On spectral clustering: Analysis and an algorithm," Advances in Neural Information Processing Systems, pp.849-856, 2002.
  16. X. Z. Fern and C. E. Brodley, "Solving cluster ensemble problems by bipartite graph partitioning," In Proc.21th Int'l Conf. Machine Learning, pp.281-288, 2004.
  17. Y. Haxhimusa and W. Kropatsch, "Segmentation graph hierarchies," In Proc. Structural, Syntactic, and Statistical Pattern Recognition, vol.3138, pp.343-351, Aug.2004.
  18. Allen Yang, John Wright, Yi Ma and Shankar Sastry, "unsupervised segmentation of natural images via lossy data compression," Computer Vision and Image Understanding, vol.110, pp.212-225, 2008. https://doi.org/10.1016/j.cviu.2007.07.005
  19. C. Pantofaru and M. Hebert, "A comparison of image segmentation algorithms," Tech. Rep. 2005.
  20. M. Meila, "Comparing clusterings: an axiomatic view", In Proc.21th Int'l Conf. Machine Learning, pp.577-584, 2005.
  21. D. Martin, C. Fowlkes, D. Tal and J. Malik, "A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics," In Proc. of IEEE International Conference on Computer Vision, pp.416-423, 2001.
  22. J.Freixenet, X.Munoz, D.Raba, J. Marti and X. Cuff, "Yet another survey on image segmentation: region and boundary information integration", In Proc. of European Conference on Computer Vision, pp.408-422, 2002.
  23. Tae Hoon Kim, Kyoung Mu Lee and Sang Uk Lee, "Learning full pairwise affinities for spectral segmentation," In Proc. of CVPR 2010, pp.2101-2108, 2010.
  24. Heekyung Yang and Kyungha Min, "Texture-based hatching for color image and video," KSII Transaction on internet and information system, vol.5, no.4, pp.763-781, 2011.
  25. I. S. Ahmad, "Texture-based image indexing and retrieval using formal concept analysis," KSII Transaction on internet and information system, vol.2, no.3, pp.150-170, 2008. https://doi.org/10.3837/tiis.2008.03.002
  26. Jingbo Zhou, Jun Yin and Zhong Jin, "A multiscale CCTA plus spectral graph partitioning for image segmentation," In CCPR2010, vol.1, pp.311-315, 2010.
  27. Xiaobai Liu et al, "Label to region by bilayer sparsity priors," In Proc. of the 17th ACM international conference on Multimedia, 2009.
  28. M.Wang, X.S.Hua, J.H.Tang and R.C.Hong, "Beyond distance measurement: constructing neighborhood similarity for video annotation," IEEE Trans. on MM, vol.11, no.3, pp.465-476, Apr.2009.
  29. M.Wang et al, "Unified video annotation via multigraph learning," IEEE Trans. on TCSVT, vol.19, no.5, pp.733-746, May.2009.
  30. K.Yang et al, "Tag tagging: towards more descriptive keywords of image content," IEEE Trans. on MULTIMEDIA, vol.13, no.4, pp.662-673, May.2011.