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. We describe the different processing steps involved in a CBR-based image segmentation scheme. The segmentation parameters of the Watershed segmentation that can be controlled are explained. One possible case description based on statistical low-level features is given as well as the similarity measure. The performance of the chosen case description and the similarity measure for retrieval is assessed based on hierarchical clustering. Finally, we propose a method for the automatic evaluation of the segmentation results that will allow us to automatically select the best segmentation parameters and, thus, making the whole segmentation scheme to a closed-loop image-segmentation control scheme.
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Frucci, M., Perner, P., Sanniti di Baja, G. (2007). Watershed Segmentation Via Case-Based Reasoning. In: Weber, R.O., Richter, M.M. (eds) Case-Based Reasoning Research and Development. ICCBR 2007. Lecture Notes in Computer Science(), vol 4626. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74141-1_29
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DOI: https://doi.org/10.1007/978-3-540-74141-1_29
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