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...

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Sharon Alpert, Meirav Galun, Ronen Basri, Achi Brandt
• Keywords: Bottom-up Aggregation Steps, Complete Hierarchical Segmentation, Computer Vision, CVPR 2007, Graph Coarsening Scheme, Image Segmentation, Segmentation Algorithms