CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2012 vol.34 Issue No.02  February
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Issue No.02  February (2012 vol.34)
pp: 315327
S. Alpert , Fac. of Math. & Comput. Sci., Weizmann Inst. of Sci., Rehovot, Israel
M. Galun , Fac. of Math. & Comput. Sci., Weizmann Inst. of Sci., Rehovot, Israel
A. Brandt , Fac. of Math. & Comput. Sci., Weizmann Inst. of Sci., Rehovot, Israel
R. Basri , Fac. of Math. & Comput. Sci., Weizmann Inst. of Sci., Rehovot, Israel
ABSTRACT
We present a bottomup aggregation approach to image segmentation. Beginning with an image, we execute a sequence of steps in which pixels are gradually merged to produce larger and larger regions. In each step, we consider pairs of adjacent regions and provide a probability measure to assess whether or not they should be included in the same segment. Our probabilistic formulation takes into account intensity and texture distributions in a local area around each region. It further incorporates priors based on the geometry of the regions. Finally, posteriors based on intensity and texture cues are combined using “ a mixture of experts” formulation. This probabilistic approach is integrated into a graph coarsening scheme, providing a complete hierarchical segmentation of the image. The algorithm complexity is linear in the number of the image pixels and it requires almost no usertuned parameters. In addition, we provide a novel evaluation scheme for image segmentation algorithms, attempting to avoid human semantic considerations that are out of scope for segmentation algorithms. Using this novel evaluation scheme, we test our method and provide a comparison to several existing segmentation algorithms.
INDEX TERMS
image texture, computational complexity, computer vision, graph theory, image segmentation, computer vision, probabilistic bottomup aggregation, cue integration, pixel merging, texture distributions, texture cues, graph coarsening scheme, hierarchical image segmentation, algorithm complexity, usertuned parameters, Image segmentation, Computer vision, Algorithm design and analysis, Probabilistic logic, Noise measurement, Partitioning algorithms, Clustering algorithms, segmentation evaluation., Computer vision, image segmentation, cue integration
CITATION
S. Alpert, M. Galun, A. Brandt, R. Basri, "Image Segmentation by Probabilistic BottomUp Aggregation and Cue Integration", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.34, no. 2, pp. 315327, February 2012, doi:10.1109/TPAMI.2011.130
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