Parameter Selection for Graph Cut Based Image Segmentation

The graph cut based approach has become very popular for interactive segmentation of the object of interest from the background. One of the most important and yet largely unsolved issues in the graph cut segmentation framework is parameter selection. Parameters are usually fixed beforehand by the developer of the algorithm. There is no single setting of parameters, however, that will result in the best possible segmentation for any general image. Usually each image has its own optimal set of parameters. If segmentation of an image is not as desired under the current setting of parameters, the user can always perform more interaction until the desired results are achieved. However, significant interaction may be required if parameter settings are far from optimal. In this paper, we develop an algorithm for automatic parameter selection. We design a measure of segmentation quality based on different features of segmentation that are combined using AdaBoost. Then we run the graph cut segmentation algorithm for different parameter values and choose the segmentation of highest quality according to our learnt measure. We develop a new way to normalize feature weights for the AdaBoost based classifier which is particularly suitable for our framework. Experimental results show a success rate of 95.6% for parameter selection.