Abstract
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 components is required. Compared to recent level-set based segmentation methods 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 results.
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Weiler, D., Eggert, J. (2008). Multi-dimensional Histogram-Based Image Segmentation. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4984. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69158-7_99
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DOI: https://doi.org/10.1007/978-3-540-69158-7_99
Publisher Name: Springer, Berlin, Heidelberg
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