English
Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue
文件大小：
2205k
资源说明：经典的图像分割算法，摘要：
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 images and compare our results to
several existing segmentation algorithms.
本源码包内暂不包含可直接显示的源代码文件，请下载源码包。
