2D image segmentation using minimum spanning trees

• Published in: Image and vision computing Vol. 15; no. 1; pp. 47 - 57
• Main Authors: Xu, Ying, Uberbacher, Edward C.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier B.V 1997; Elsevier
• Subjects: Algorithmics. Computability. Computer arithmetics; Applied sciences; Artificial intelligence; Computer science; control theory; systems; Dynamic programming; Exact sciences and technology; Image segmentation; Minimum spanning trees; Pattern recognition. Digital image processing. Computational geometry; Software; Software engineering; Theoretical computing; Tree partitioning; Image segmentation; Dynamic programming; Minimum spanning trees; Tree partitioning; Partition; Segmentation; Image processing; Spanning tree
• DOI: 10.1016/S0262-8856(96)01105-5
• EISSN: 1872-8138
• ISSN: 0262-8856

This paper presents a new algorithm for partitioning a gray-level image into connected homogeneous regions. The novelty of this algorithm lies in the fact that, by constructing a minimum spanning tree representation of a gray-level image, it reduces a region partitioning problem to a minimum spanning tree partitioning problem, and hence reduces the computational complexity of the region partitioning problem. The tree-partitioning algorithm, in essence, partitions a minimum spanning tree into subtrees, representing different homogeneous regions, by minimizing the sum of variations of gray levels over all subtrees under the constraints that each subtree should have at least a specified number of nodes, and two adjacent subtrees should have significantly different average gray-levels. Two (faster) heuristic implementations are also given for large-scale region partitioning problems. Test results have shown that the segmentation results are satisfactory and insensitive to noise.