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Multiresolution Color Image Segmentation
July 1994 (vol. 16 no. 7)
pp. 689-700

Image segmentation is the process by which an original image is partitioned into some homogeneous regions. In this paper, a novel multiresolution color image segmentation (MCIS) algorithm which uses Markov random fields (MRF's) is proposed. The proposed approach is a relaxation process that converges to the MAP (maximum a posteriori) estimate of the segmentation. The quadtree structure is used to implement the multiresolution framework, and the simulated annealing technique is employed to control the splitting and merging of nodes so as to minimize an energy function and therefore, maximize the MAP estimate. The multiresolution scheme enables the use of different dissimilarity measures at different resolution levels. Consequently, the proposed algorithm is noise resistant. Since the global clustering information of the image is required in the proposed approach, the scale space filter (SSF) is employed as the first step. The multiresolution approach is used to refine the segmentation. Experimental results of both the synthesized and real images are very encouraging. In order to evaluate experimental results of both synthesized images and real images quantitatively, a new evaluation criterion is proposed and developed.

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Index Terms:
image segmentation; simulated annealing; Markov processes; tree data structures; multiresolution color image segmentation; Markov random fields; relaxation process; maximum a posteriori estimate; quadtree structure; simulated annealing; splitting; merging; dissimilarity measures; noise resistant; global clustering information; scale space filter; real images; synthesized images; evaluation criterion
Citation:
J. Liu, Y.H. Yang, "Multiresolution Color Image Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 7, pp. 689-700, July 1994, doi:10.1109/34.297949
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