Today I read a paper titled “Automatic Image Segmentation by Dynamic Region Merging”
The abstract is:
This paper addresses the automatic image segmentation problem in a region merging style
With an initially over-segmented image, in which the many regions (or super-pixels) with homogeneous color are detected, image segmentation is performed by iteratively merging the regions according to a statistical test
There are two essential issues in a region merging algorithm: order of merging and the stopping criterion
In the proposed algorithm, these two issues are solved by a novel predicate, which is defined by the sequential probability ratio test (SPRT) and the maximum likelihood criterion
Starting from an over-segmented image, neighboring regions are progressively merged if there is an evidence for merging according to this predicate
We show that the merging order follows the principle of dynamic programming
This formulates image segmentation as an inference problem, where the final segmentation is established based on the observed image
We also prove that the produced segmentation satisfies certain global properties
In addition, a faster algorithm is developed to accelerate the region merging process, which maintains a nearest neighbor graph in each iteration
Experiments on real natural images are conducted to demonstrate the performance of the proposed dynamic region merging algorithm