One-Class Support Vector Machines for Aerial Images Segmentation
Interpretation of aerial images is an important task in
various applications. Image segmentation can be viewed as the essential
step for extracting information from aerial images. Among many
developed segmentation methods, the technique of clustering has been
extensively investigated and used. However, determining the number
of clusters in an image is inherently a difficult problem, especially
when a priori information on the aerial image is unavailable. This
study proposes a support vector machine approach for clustering
aerial images. Three cluster validity indices, distance-based index,
Davies-Bouldin index, and Xie-Beni index, are utilized as quantitative
measures of the quality of clustering results. Comparisons on the
effectiveness of these indices and various parameters settings on the
proposed methods are conducted. Experimental results are provided
to illustrate the feasibility of the proposed approach.
[1] Y. Hata, S. Kobashi, S. Hirano, H. Kitagaki, and E. Mori, "Automated
segmentation of human brain MR images aided by fuzzy information
granulation and fuzzy inference," IEEE Transactions on Systems, Man,
and Cybernetics, Part C: Applications and Reviews, vol. 30, no. 3, pp.
381-395, Aug 2000.
[2] V. Letournel, B. Sankur, F. Pradeilles, and H. Maˆıtre, "Feature extraction
for quality assessment of aerial image segmentation," in Proceedings of
the ISPRS Technical Commission III Symposium 2002, Photogrammetric
Computer Vision (PCV-02), Graz, Austria, 2002, pp. 141-163.
[3] G. Cao, Z. Mao, X. Yang, and D. Xia, "Optical aerial image partitioning
using level sets based on modified chan-vese model," Pattern Recognition
Letters, vol. 29, no. 4, pp. 457 - 464, 2008.
[4] Z. Iscan, A. Yksel, Z. Dokur, M. Korrek, and T. lmez, "Medical image
segmentation with transform and moment based features and incremental
supervised neural network," Digital Signal Processing, vol. 19, no. 5,
pp. 890 - 901, 2009.
[5] S. Kavitha, S. M. M. Roomi, and N. Ramaraj, "Lossy compression
through segmentation on low depth-of-field images," Digital Signal
Processing, vol. 19, no. 1, pp. 59 - 65, 2009.
[6] A. K. Jain, M. N. Murty, and P. J. Flynn, "Data clustering: a review,"
ACM Computing Surveys, vol. 31, no. 3, pp. 264-323, 1999.
[7] J. T. Tou and R. C. Gonzalez, Pattern Recognition Principles. Addison-
Wesley Pub. Co., Reading, Mass.,, 1974.
[8] M. Herbin, N. Bonnet, and P. Vautrot, "Estimation of the number of
clusters and influences zones," Pattern Recognition Letters, vol. 22,
no. 14, pp. 1557-1568, 2001.
[9] J. Kang, L. Min, Q. Luan, X. Li, and J. Liu, "Novel modified fuzzy cmeans
algorithm with applications," Digital Signal Processing, vol. 19,
no. 2, pp. 309 - 319, 2009.
[10] N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector
Machines and Other Kernel-Based Learning Methods. New Jersey:
Cambridge University Press, 2000.
[11] D. L. Davies and D. W. Bouldin, "A cluster separation measure," IEEE
Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-
1, no. 2, pp. 224-227, 1979.
[12] X. Xie and G. Beni, "A validity measure for fuzzy clustering," IEEE
Transactions on Pattern Analysis and Machine Intelligence, vol. 13,
no. 8, pp. 841-847, 1991.
[13] S. Lee and M. M. Crawford, "Unsupervised multistage image classification
using hierarchical clustering with a bayesian similarity measure,"
IEEE Transactions on Image Processing, vol. 14, no. 3, pp. 312-320,
2005.
[14] Y. Xia, D. Feng, T. Wang, R. Zhao, and Y. Zhang, "Image segmentation
by clustering of spatial patterns," Pattern Recognition Letters, vol. 28,
no. 12, pp. 1548-1555, 2007.
[15] S. Das and A. Konar, "Automatic image pixel clustering with an
improved differential evolution," Applied Soft Computing, vol. 9, no. 1,
pp. 226-236, 2009.
[16] B. Sch¨olkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, and R. C.
Williamson, "Estimating the Support of a High-Dimensional Distribution,"
Neural Computation, vol. 13, no. 7, pp. 1443-1472, July 2001.
[17] D. Li, R. M. Mersereau, and S. Simske, "Blind image deconvolution
through support vector regression," IEEE Transactions on Neural Networks,
vol. 18, no. 3, pp. 931-935, 2007.
[18] Z. L. Wu, C. H. Li, J. K. Y. Ng, and K. R. Leung, "Location estimation
via support vector regression," IEEE Transactions on Mobile Computing,
vol. 6, pp. 311-321, 2007.
[19] L. Cao, "Support vector machines experts for time series forecasting,"
Neurocomputing, vol. 51, pp. 321-339, 2003.
[20] C.-C. Chang and C.-J. Lin, "LIBSVM: a library for support vector
machines," http://www.csie.ntu.edu.tw/Ôê╝cjlin/libsvm, 2001.
[21] R. Haralick and L. G. Dhspito, "Image segmentation techniques,"
Applications of Artificial Intelligence II, vol. 548, pp. 2-9, 1985.
[1] Y. Hata, S. Kobashi, S. Hirano, H. Kitagaki, and E. Mori, "Automated
segmentation of human brain MR images aided by fuzzy information
granulation and fuzzy inference," IEEE Transactions on Systems, Man,
and Cybernetics, Part C: Applications and Reviews, vol. 30, no. 3, pp.
381-395, Aug 2000.
[2] V. Letournel, B. Sankur, F. Pradeilles, and H. Maˆıtre, "Feature extraction
for quality assessment of aerial image segmentation," in Proceedings of
the ISPRS Technical Commission III Symposium 2002, Photogrammetric
Computer Vision (PCV-02), Graz, Austria, 2002, pp. 141-163.
[3] G. Cao, Z. Mao, X. Yang, and D. Xia, "Optical aerial image partitioning
using level sets based on modified chan-vese model," Pattern Recognition
Letters, vol. 29, no. 4, pp. 457 - 464, 2008.
[4] Z. Iscan, A. Yksel, Z. Dokur, M. Korrek, and T. lmez, "Medical image
segmentation with transform and moment based features and incremental
supervised neural network," Digital Signal Processing, vol. 19, no. 5,
pp. 890 - 901, 2009.
[5] S. Kavitha, S. M. M. Roomi, and N. Ramaraj, "Lossy compression
through segmentation on low depth-of-field images," Digital Signal
Processing, vol. 19, no. 1, pp. 59 - 65, 2009.
[6] A. K. Jain, M. N. Murty, and P. J. Flynn, "Data clustering: a review,"
ACM Computing Surveys, vol. 31, no. 3, pp. 264-323, 1999.
[7] J. T. Tou and R. C. Gonzalez, Pattern Recognition Principles. Addison-
Wesley Pub. Co., Reading, Mass.,, 1974.
[8] M. Herbin, N. Bonnet, and P. Vautrot, "Estimation of the number of
clusters and influences zones," Pattern Recognition Letters, vol. 22,
no. 14, pp. 1557-1568, 2001.
[9] J. Kang, L. Min, Q. Luan, X. Li, and J. Liu, "Novel modified fuzzy cmeans
algorithm with applications," Digital Signal Processing, vol. 19,
no. 2, pp. 309 - 319, 2009.
[10] N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector
Machines and Other Kernel-Based Learning Methods. New Jersey:
Cambridge University Press, 2000.
[11] D. L. Davies and D. W. Bouldin, "A cluster separation measure," IEEE
Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-
1, no. 2, pp. 224-227, 1979.
[12] X. Xie and G. Beni, "A validity measure for fuzzy clustering," IEEE
Transactions on Pattern Analysis and Machine Intelligence, vol. 13,
no. 8, pp. 841-847, 1991.
[13] S. Lee and M. M. Crawford, "Unsupervised multistage image classification
using hierarchical clustering with a bayesian similarity measure,"
IEEE Transactions on Image Processing, vol. 14, no. 3, pp. 312-320,
2005.
[14] Y. Xia, D. Feng, T. Wang, R. Zhao, and Y. Zhang, "Image segmentation
by clustering of spatial patterns," Pattern Recognition Letters, vol. 28,
no. 12, pp. 1548-1555, 2007.
[15] S. Das and A. Konar, "Automatic image pixel clustering with an
improved differential evolution," Applied Soft Computing, vol. 9, no. 1,
pp. 226-236, 2009.
[16] B. Sch¨olkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, and R. C.
Williamson, "Estimating the Support of a High-Dimensional Distribution,"
Neural Computation, vol. 13, no. 7, pp. 1443-1472, July 2001.
[17] D. Li, R. M. Mersereau, and S. Simske, "Blind image deconvolution
through support vector regression," IEEE Transactions on Neural Networks,
vol. 18, no. 3, pp. 931-935, 2007.
[18] Z. L. Wu, C. H. Li, J. K. Y. Ng, and K. R. Leung, "Location estimation
via support vector regression," IEEE Transactions on Mobile Computing,
vol. 6, pp. 311-321, 2007.
[19] L. Cao, "Support vector machines experts for time series forecasting,"
Neurocomputing, vol. 51, pp. 321-339, 2003.
[20] C.-C. Chang and C.-J. Lin, "LIBSVM: a library for support vector
machines," http://www.csie.ntu.edu.tw/Ôê╝cjlin/libsvm, 2001.
[21] R. Haralick and L. G. Dhspito, "Image segmentation techniques,"
Applications of Artificial Intelligence II, vol. 548, pp. 2-9, 1985.
@article{"International Journal of Electrical, Electronic and Communication Sciences:49827", author = "Chih-Hung Wu and Chih-Chin Lai and Chun-Yen Chen and Yan-He Chen", title = "One-Class Support Vector Machines for Aerial Images Segmentation", abstract = "Interpretation of aerial images is an important task in
various applications. Image segmentation can be viewed as the essential
step for extracting information from aerial images. Among many
developed segmentation methods, the technique of clustering has been
extensively investigated and used. However, determining the number
of clusters in an image is inherently a difficult problem, especially
when a priori information on the aerial image is unavailable. This
study proposes a support vector machine approach for clustering
aerial images. Three cluster validity indices, distance-based index,
Davies-Bouldin index, and Xie-Beni index, are utilized as quantitative
measures of the quality of clustering results. Comparisons on the
effectiveness of these indices and various parameters settings on the
proposed methods are conducted. Experimental results are provided
to illustrate the feasibility of the proposed approach.", keywords = "Aerial imaging, image segmentation, machine learning,support vector machine, cluster validity index", volume = "4", number = "5", pages = "767-6", }
