COLOR AND TEXTURE BASED SEGMENTATION ALGORITHM FOR MULTICOLOR TEXTURED IMAGES

Irene Fondón, Carmen Serrano, Begoña Acha

2007

Abstract

We propose a color-texture image segmentation algorithm based on multistep region growing. This algorithm is able to deal with multicolored textures. Each of the colors in the texture to be segmented is considered as reference color. In this algorithm color and texture information are extracted from the image by the construction of color distances images, one for each reference color, and a texture energy image. The color distance images are formed by calculating CIEDE2000 distance in the L*a*b* color space to the colors that compound the multicolored texture. The texture energy image is extracted from some statistical moments. The method segment the color information by means of an adaptative N-dimensional region growing where N is the number of reference colors. The tolerance parameter is increased iteratively until an optimum is found and its growth is determined by a step size which depends on the variance on each distance image for the actual grown region. The criterium to decide which is the optimum value of the tolerance parameter depends on the contrast along the edge of the region grown, choosing the one which provides the region with the highest mean contrast in relation to the background. Additionally, this color multistep region growing is texture-controlled, in the sense that an extra condition to include a particular pixel in a region is demanded: the pixel needs to have the same texture as the rest of the pixels within the region. Results prove that the proposed method works very well with general purpose images and significantly improves the results obtained with other previously published algorithm (Fondón et al, 2006).

References

  1. Fondón I., Serrano C., Acha B.,2006. Color-Texture Image Segmentation based on Multi-Step Region Growing. Optical Engineering, The International Society for Optical Engineering (SPIE), Vol. 45. 057002-9,057002-9
  2. Muñoz, X., 2002. Image segmentation integrating colour, texture and boundary information. PhD Thesis, Universitat de Girona , Spain.
  3. Hojjatoleslami, S. A., Kittler, J., 1998. Region growing: a new approach. In IEEE Trans. on Image Processing, 7(7), 1079-1084.
  4. Adams, R., Bischof, L., 1994. Seeded region growing, In IEEE Trans. on Pattern Analysis and Machine Intelligence, 16(6), 641-647.
  5. Fan, J., Yau, D. K. Y., Elmagarmid, A. K., Aref, W.G., 2001. Automatic image segmentation by integrating color-edge extraction and seeded region growing. In IEEE Trans. on Image Processing, 10(10), 1454-1466.
  6. Fan, J., Zeng, G., Body, M., Hacid, M.-S., 2005. Seeded region growing: an extensive and comparative study. Pattern Recognition Letters, 26, 1139-1156.
  7. Cheng, S. -C., 2003. Region-growing approach to colour segmentation using 3-D clustering and relaxation labeling. In IEEE Proc. -Vis. Image Signal Process, 150(4), 270-276.
  8. Maeda, J., Novianto, S., Saga, S., Suzuki, Y., Anh, V. V., 1999. Rough and accurate segmentation of natural images using fuzzy region-growing algorithm. Proc. Int. Conf. on Image Processing, Kobe (Japan), 3, 227- 231.
  9. Hao, X., Bruce, C., Pislaru, C., Greenleaf, J. F., 2000. A novel region growing method for segmenting ultrasound images. In IEEE Int. Ultrasonics Symposium, 1717-1720
  10. Pohle, R., Toennies, K. D., 2001. Segmentation of medical images using adaptative region growin. In Proc. SPIE Medical Imaging, 4322-4331.
  11. Plataniotis, K.N., Venetsanopoulos, A.N., 2000. Color image processing and applications, Springer, Berlin (Germany), 35-37.
  12. Perona, P., Malik, J., 1990. Scale-space and edge detection using anisotropic diffusion. In IEEE Trans. on Pattern Analysis and Machine Intelligence, 7, 629- 639.
  13. Maulik, U., Bandyopadhyay, S., 2002. Performance evaluation of some clustering algorithms and validity indices. In IEEE Trans. on Pattern Analysis and Machine Intelligence, 24(12), 1650-1654.
  14. Luo, M.R., Cui, G, Rigg, B., 2001. The development of the CIE 2000 Colour-Difference formula: CIEDE2000. Colour Research and Application, 26(5), 340-350.
  15. Poirson, B., Wandell, B., 1996. Pattern-color separable pathways predict sensitivity to simple colored patterns. Vision Res., 36(4), 515-526.
  16. Mojsilovic, A., Kovacevic, J., Kall, D., Safranek, R., Ganapathy, S., 2000. Matching and retrieval based on the vocabulary and grammar of color patterns. In IEEE Trans. Image Processing, 9(1), 38-54.
  17. Mäenpää, T., Pietikäinen, M., 2004. Classification with color and texture: jointly or separately?. In Pattern Recognition, 37, 1629-1640.
  18. Tuceryan, M., 1994. Moment based texture segmentation. In Pattern recognition letters, 15(7), 659-668.
  19. Zamperoni, P., 1995. Model-free texture segmentation based on distances between first-order statistics. Digital Signal Processing, 5, 197-225.
  20. Lowitz, G., 1983. Can a local histogram really map texture information?. In Pattern Recognition, 2, 141- 147.
  21. Kim, V., Yaroslavskii, Y. P.,1986. Rank algorithms for picture processing. Comput. Vision Graphics Image Process., 35, 234-258.
  22. Acha, B., Serrano, C., Acha, J. I., Roa, L. M., 2003. CAD tool for burn diagnosis, Lecture Notes in Computer Science (Springer), 2732, 294-305.
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Paper Citation


in Harvard Style

Fondón I., Serrano C. and Acha B. (2007). COLOR AND TEXTURE BASED SEGMENTATION ALGORITHM FOR MULTICOLOR TEXTURED IMAGES . In Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, ISBN 978-972-8865-73-3, pages 258-263. DOI: 10.5220/0002042502580263


in Bibtex Style

@conference{visapp07,
author={Irene Fondón and Carmen Serrano and Begoña Acha},
title={COLOR AND TEXTURE BASED SEGMENTATION ALGORITHM FOR MULTICOLOR TEXTURED IMAGES},
booktitle={Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,},
year={2007},
pages={258-263},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002042502580263},
isbn={978-972-8865-73-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,
TI - COLOR AND TEXTURE BASED SEGMENTATION ALGORITHM FOR MULTICOLOR TEXTURED IMAGES
SN - 978-972-8865-73-3
AU - Fondón I.
AU - Serrano C.
AU - Acha B.
PY - 2007
SP - 258
EP - 263
DO - 10.5220/0002042502580263