Transactions on Case-Based Reasoning
Volume 2 - Number 1 - October 2009 - Pages 41-53
A Study on the Case Image Description for Learning the Model of the Watershed Segmentation
A. Attig and P. Perner
Institute of Computer Vision and Applied Computer Sciences, IBAI, Leipzig, Germany
Many image analysis methods need a lot of parameters that have to be adjusted to the particular image in order to achieve the best results. Therefore, methods for parameter learning are required that can assist a system developer in building a model. This task is usually called meta-learning. One problem in meta-learning is to describe the properties of the input so that it can be properly mapped to the parameters. In this paper, we consider this task for image segmentation based on the watershed transformation. We use Case-Based Reasoning to control the parameter selection process. Our previous investigation on the theoretical and implementation aspects of the watershed transformation allowed us to draw conclusions for suitable image descriptions. Four different descriptions have been considered based on: statistical and texture features; marginal distributions of columns, rows, and diagonals; similarity between the regional minima; and central moments. The two descriptions based on statistical and texture features and on central moments resulted to be the best ones for segmentation based on watershed transformation. They can best separate the cases into groups having the same segmentation parameters and work nicely also for rotated and rescaled images.
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