Abstract
We present a novel algorithm for segmentation of natural images that harnesses the principle of minimum description length (MDL). Our method is based on observations that a homogeneously textured region of a natural image can be well modeled by a Gaussian distribution and the region boundary can be effectively coded by an adaptive chain code. The optimal segmentation of an image is the one that gives the shortest coding length for encoding all textures and boundaries in the image, and is obtained via an agglomerative clustering process applied to a hierarchy of decreasing window sizes as multi-scale texture features. The optimal segmentation also provides an accurate estimate of the overall coding length and hence the true entropy of the image. We test our algorithm on the publicly available Berkeley Segmentation Dataset. It achieves state-of-the-art segmentation results compared to other existing methods.
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Research was supported in part by NSF IIS 07-03756, ONR N00014-09-1-0230, ARO MURI W911NF-06-1-0076, and ARL MAST-CTA W911NF-08-2-0004. Hossein Mobahi was supported by Computational Science & Engineering (CSE) Ph.D. fellowship of University of Illinois at Urbana-Champaign. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory, US Government, or the CSE program. The US Government is authorized to reproduce and distribute for Government purposes notwithstanding any copyright notation hereon.
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Mobahi, H., Rao, S.R., Yang, A.Y. et al. Segmentation of Natural Images by Texture and Boundary Compression. Int J Comput Vis 95, 86–98 (2011). https://doi.org/10.1007/s11263-011-0444-0
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DOI: https://doi.org/10.1007/s11263-011-0444-0