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2007
IEEE

Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration

11 years 11 days ago
Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration
We present a parameter free approach that utilizes multiple cues for image segmentation. Beginning with an image, we execute a sequence of bottom-up aggregation 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 user-tuned parameters. We test our method on a variety of gray scale image...
Sharon Alpert, Meirav Galun, Ronen Basri, Achi Bra
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2007
Where CVPR
Authors Sharon Alpert, Meirav Galun, Ronen Basri, Achi Brandt
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