Algorithms for clustering data
Algorithms for clustering data
A New Metric for Grey-Scale Image Comparison
International Journal of Computer Vision
Yet Another Survey on Image Segmentation: Region and Boundary Information Integration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
CBR-Based Ultra Sonic Image Interpretation
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
How Dissimilar Are Two Grey-Scale Images?
Mustererkennung 1995, 17. DAGM-Symposium
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Image processing in case-based reasoning
The Knowledge Engineering Review
Detecting and ranking foreground regions in gray-level images
BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
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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.