Commenced in January 2007
Paper Count: 30174
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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1328888Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1556
 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.
 V. Letournel, B. Sankur, F. Pradeilles, and H. Ma╦å─▒tre, "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.
 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.
 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.
 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.
 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.
 J. T. Tou and R. C. Gonzalez, Pattern Recognition Principles. Addison- Wesley Pub. Co., Reading, Mass.,, 1974.
 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.
 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.
 N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. New Jersey: Cambridge University Press, 2000.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 L. Cao, "Support vector machines experts for time series forecasting," Neurocomputing, vol. 51, pp. 321-339, 2003.
 C.-C. Chang and C.-J. Lin, "LIBSVM: a library for support vector machines," http://www.csie.ntu.edu.tw/Ôê╝cjlin/libsvm, 2001.
 R. Haralick and L. G. Dhspito, "Image segmentation techniques," Applications of Artificial Intelligence II, vol. 548, pp. 2-9, 1985.