Daniel Weiler and Julian Eggert, "Multi-Dimensional Histogram-Based Image Segmentation", Proceedings of the 14th International Conference on Neural Information Processing (ICONIP), 2007.

In this paper we present an approach for multi-dimensional histogram-based image segmentation. We combine level-set methods for image segmentation with probabilistic region descriptors based on multi- dimensional histograms. Unlike stated by other authors we show that colour space histograms provide a reasonable and efficient description of image regions. In contrast to Gaussian Mixture Model based algorithms no parameter learning and estimation of the number of mixture compo- nents is required. Compared to recent level-set based segmentation meth- ods satisfying segmentation results are achieved without specific features (e.g. texture). In a comparison with state-of-the-art image segmentation methods it is shown that the proposed approach yields competitive re- sults.