Abstract
In this paper we improve image segmentation based on texture properties. The already good results achieved using learned dictionaries and Gaussian smoothing are improved by minimizing an energy function that has the form of a Potts model. The proposed \(\alpha \)-erosion method is a greedy method that essentially relabels the pixels one by one and is computationally very fast. It can be used in addition to, or instead of, Gaussian smoothing to regularize the label images in supervised texture segmentation problems. The proposed \(\alpha \)-erosion method achieves excellent results on a much used set of test images: on average we get 2.9 % wrongly classified pixels. Gaussian smoothing gives 10 % and the best results reported earlier give 4.5 %.
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Skretting, K., Engan, K. (2014). Energy Minimization by \(\alpha \)-Erosion for Supervised Texture Segmentation. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2014. Lecture Notes in Computer Science(), vol 8814. Springer, Cham. https://doi.org/10.1007/978-3-319-11758-4_23
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DOI: https://doi.org/10.1007/978-3-319-11758-4_23
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