Multiresolutional Cluster Segmentation Using Spatial Context
A multiresolutional cluster/relaxation image segmentation algorithm is described. A preliminary split-merge procedure generates variable-sized quadtree-blocks. These multiresolutional units are used in the subsequent clustering. A probabilistic relaxation procedure conducts the final labeling. A large reduction in data processing is attained by processing blocks rather than pixels, while still yielding good segmentation results.
Unable to display preview. Download preview PDF.
- Nagin P.A., “Segmentation Using Spatial Context and Feature Space Cluster Labels,” Univ. of Massachusetts, COINS Techn. Report 78–8, May 1978.Google Scholar
- Gerbrands J.J., Backer E., “Split-and-merge Segmentation of SLAR-imagery: Segmentation Consistency,” Proc. 7ICPR, Montreal, Canada, 1984, pp. 284–286.Google Scholar
- Ball G.H., Hall D.J., “ISODATA — A Novel Method of Data Analysis and Pattern Classification,” Techn. Report SRI, California, 1965.Google Scholar
- Zucker S.W., Hummel R.A., Rosenfeld A., “An Application of Relaxation Labeling to Line and Curve Enhancement,” IEEE Trans. Comp., Vol, C-26, No. 4, April 1977, pp. 394–403.Google Scholar