The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Multiresolution color image segmentation
Abstract
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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Cites
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
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Cited by
Image segmentation evaluation: A survey of unsupervised methods
Zhang, H., Fritts, J. E., & Goldman, S. A. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2007.08.003 · Cited by 963
Unsupervised Performance Evaluation of Image Segmentation
Chabrier, S., Emile, B., Rosenberger, C., & Laurent, H. (2006) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/asp/2006/96306 · Cited by 136
New benchmark for image segmentation evaluation
Ge, F., Wang, S., & Liu, T. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2762250 · Cited by 108
Meta-Evaluation of Image Segmentation Using Machine Learning
Zhang, H., Cholleti, S., Goldman, S. A., & Fritts, J. E. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06) · doi:10.1109/cvpr.2006.185 · Cited by 32
Metadata
- Authors
- Liu, J., & Yang, Y.-H.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1994-07
- Volume
- 16
- Issue
- 7
- Pages
- 689-700
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2145433796
- Cited by
- 485
- References
- 15
- FWCI
- 15.48
- Type
- journal article
- Language
- en
- Open access
- closed
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