The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
PARTICLE SWARM OPTIMIZATION METHOD FOR IMAGE CLUSTERING
Abstract
An image clustering method that is based on the particle swarm optimizer (PSO) is developed in this paper. The algorithm finds the centroids of a user specified number of clusters, where each cluster groups together with similar image primitives. To illustrate its wide applicability, the proposed image classifier has been applied to synthetic, MRI and satellite images. Experimental results show that the PSO image classifier performs better than state-of-the-art image classifiers (namely, K-means, Fuzzy C-means, K-Harmonic means and Genetic Algorithms) in all measured criteria. The influence of different values of PSO control parameters on performance is also illustrated.
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Cites
Kennedy, J., & Eberhart, R. (2002) · Proceedings of ICNN'95 - International Conference on Neural Networks · doi:10.1109/icnn.1995.488968 · Cited by 58,192
(1967) · Cited by 23,719
Neural networks for pattern recognition
(1994) · Choice Reviews Online · doi:10.5860/choice.31-5500 · Cited by 18,538
Kohonen, T. (2001) · Springer Series in Information Sciences · doi:10.1007/978-3-642-56927-2 · Cited by 16,019
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287
A modified particle swarm optimizer
Shi, Y., & Eberhart, R. (2002) · 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360) · doi:10.1109/icec.1998.699146 · Cited by 10,229
Pattern Recognition with Fuzzy Objective Function Algorithms
Bezdek, J. C. (1981) · doi:10.1007/978-1-4757-0450-1 · Cited by 9,881
Remote Sensing and Image Interpretation
Wright, J., Lillesand, T. M., & Kiefer, R. W. (1980) · The Geographical Journal · doi:10.2307/634969 · Cited by 7,238
Zhang, Y., Brady, M., & Smith, S. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.906424 · Cited by 6,982
The EM Algorithm and Extensions
Kushary, D. (1998) · Technometrics · doi:10.1080/00401706.1998.10485534 · Cited by 5,417
Metadata
- Authors
- OMRAN, M., ENGELBRECHT, A. P., & SALMAN, A.
- Venue
- International Journal of Pattern Recognition and Artificial Intelligence
- Publisher
- World Scientific Pub Co Pte Ltd
- Published
- 2005-05
- Volume
- 19
- Issue
- 03
- Pages
- 297-321
- ISSN
- 0218-0014 (ISSN-L)
- 1793-6381
- OpenAlex
- W2102236449
- Cited by
- 288
- References
- 36
- FWCI
- 10.31
- Type
- journal article
- Language
- en
- Open access
- closed
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- OMRAN, M., ENGELBRECHT, A. P., & SALMAN, A. (2005). PARTICLE SWARM OPTIMIZATION METHOD FOR IMAGE CLUSTERING. International Journal of Pattern Recognition and Artificial Intelligence. https://doi.org/10.1142/s0218001405004083
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