Abstract
This paper proposes a novel grey-level image segmentation scheme employing case-based reasoning. Segmentation is accomplished by using the watershed transformation, which provides a partition of the image into regions whose contours closely fit those perceived by human users. Case-based reasoning is used to select the segmentation parameters involved in the segmentation algorithm by taking into account the features characterizing the current image. Preliminarily, a number of images are analyzed and the parameters producing the best segmentation for each image, found empirically, are recorded. These images are grouped to form relevant cases, where each case includes all images having similar image features, under the assumption that the same segmentation parameters will produce similarly good segmentation results for all images in the case.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Pal, N.R., Pal, S.K.: A review on image segmentation techniques. Pattern Recognition 26(9), 1277–1294 (1993)
Pham, D.L., Xu, C., Prince, J.L.: Current methods in medical image segmentation. Annual Review of Biomedical Engineering 2, 315–337 (2000)
Lucchese, L., Mitra, S.K.: Color Image Segmentation: A State-of-the-Art Survey. In: Image Processing, Vision, and Pattern Recognition. Proc. of the Indian National Science Academy (INSA-A), New Delhi, India, vol. 67 A(2), pp. 207–221 (2001)
Cheng, H.D., Jiang, X.H., Sun, Y., Wang, J.: Color image segmentation: advances and prospects. Pattern Recognition 34, 2259–2281 (2001)
Freixenet, J., Muñoz, X., Raba, D., MartÃ, J., CufÃ, X.: Yet Another Survey on Image Segmentation: Region and Boundary Information Integration. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 408–422. Springer, Heidelberg (2002)
Beucher, S., Lantuejoul, C.: Use of watersheds in contour detection. In: Proc. Int. Workshop on Image Processing, Real-Time Edge and Motion Detection/Estimation, Rennes, France (1979)
Beucher, S., Meyer, F.: The morphological approach of segmentation: the watershed transformation. In: Dougherty, E. (ed.) Mathematical Morphology in Image Processing, Marcel Dekker, New York, pp. 433–481 (1993)
Perner, P.: An Architecture for a CBR Image Segmentation System. Journal on Engineering Application in Artificial Intelligence 12(6), 749–759 (1999)
Perner, P.: CBR Ultra Sonic Image Interpretation. In: Blanzieri, E., Portinale, L. (eds.) EWCBR 2000. LNCS (LNAI), vol. 1898, pp. 479–481. Springer, Heidelberg (2000)
Perner, P.: Are case-based reasoning and dissimilarity-based classification two sides of the same coin? Journal Engineering Applications of Artificial Intelligence 15(3), 205–216 (2002)
Perner, P., Perner, H., Müller, B.: Similarity Guided Learning of the Case Description and Improvement of the System Performance in an Image Classification System. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, pp. 604–612. Springer, Heidelberg (2002)
Frucci, M.: Oversegmentation Reduction by Flooding Regions and Digging Watershed Lines. International Journal of Pattern Recognition and Artificial Intelligence 20(1), 15–38 (2006)
Frucci, M., Arcelli, C., Sanniti di Baja, G.: Detecting and ranking foreground regions in gray-level images. In: De Gregorio, M., Di Maio, V., Frucci, M., Musio, C. (eds.) BVAI 2005. LNCS, vol. 3704, pp. 406–415. Springer, Heidelberg (2005)
Zamperoni, P., Starovoitov, V.: How dissimilar are two gray-scale images. In: Proceedings of the 17th DAGM Symposium, pp. 448–455. Springer, Heidelberg (1995)
Wilson, D.L., Baddeley, A.J., Owens, R.A.: A new metric for grey-scale image comparision. International Journal of Computer Vision 24(1), 1–29 (1997)
Dreyer, H., Sauer, W.: Prozeßanalyse. Verlag Technik, Berlin (1982)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Frucci, M., Perner, P., di Baja, G.S. (2007). Watershed Segmentation Via Case-Based Reasoning. In: Mele, F., Ramella, G., Santillo, S., Ventriglia, F. (eds) Advances in Brain, Vision, and Artificial Intelligence. BVAI 2007. Lecture Notes in Computer Science, vol 4729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75555-5_23
Download citation
DOI: https://doi.org/10.1007/978-3-540-75555-5_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75554-8
Online ISBN: 978-3-540-75555-5
eBook Packages: Computer ScienceComputer Science (R0)