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Scenery Image Segmentation Using Support Vector Machines

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Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents an effective and efficient method for solving scenery image segmentation by applying the SVMs methodology. Scenery image segmentation is regarded as a data classification problem, and is effectively answered by the proposed method in this paper. Using the model selection in our system architecture, our system is relatively simple compared to other conventional heuristic image segmentation approaches yet demonstrates promising classification results.
Wydawca
Rocznik
Strony
379--388
Opis fizyczny
Bibliogr. 16 poz., tab., wykr.
Twórcy
autor
  • Department of Computer Science and Information Engineering, National Chung Cheng Uniersity, Chiayi, Taiwan, 621, R.O.C.
autor
  • Department of Computer Science and Information Engineering, National Chung Cheng Uniersity, Chiayi, Taiwan, 621, R.O.C.
Bibliografia
  • [1] Carson. C. Bclongie, S., Greenspan. H., Malik. J.: Blobworld: image segmentation using expectation maximizalion and its application to image querying. IEEE Trans. Pattern Anal. Machine Intell., 24(8), 2002, 1026-1038.
  • [2] Cheng. H. D., Jiang, X. H., Wang, J. L.: Color image segmentation based on homogram thresholding and region merging. Pattern Recognition, 35(2), 2002 373-393.
  • [3] Bellon. O. R. P., Silva. L.: New improvements to range image segmentation by edge detection, IEEE Signal Processing Lett., 9(2), 2002. 43-45.
  • [4] Kuntimad, G., Ranganath. H. S.: Perfect image segmentation using pulse coupled neural networks, IEEE Trans. Neural Networks, 10(3). 1999, 591-598.
  • [5] Shen, X., Spann, M., Nacken. P.: Segmentation of 2D and 3D images through a hierarchical clustering based on region modelling, Pattern Recognition, 31(9). 1998, 1295-1309.
  • [6] Wong. K. P., Feng, D., Meikle, S. R., Fulham. M. J.: Segmentation of dynamic PET images using cluster analysis. IEEE Trans. Nucl. Sci., 49(I). 2002, 200-207.
  • [7] Zugaj. D., Lattuati. V: A new approach of color images segmentation based on fusing region and edge segmentations Pattern Recognition. 31(2), 1998. 105-113.
  • [8] Cheng, H. D., Jiang, X. H., Sun, Y., Wang, J.: Color image segmentation; advances and prospects, Pattern Recognition, 34(12), 2001, 2259-2281.
  • [9] Vapnik, V. N.: The Nature of Statistical Learning Theory, New York: Springer-Verlag, 1995.
  • [10] Vapnik, V. N.: Statistical Learning Theory, New York: Wiley, 1998.
  • [11] Hsu, C. W., Lin, C. J.: A comparison of methods for multiclass support vector machines, IEE Trans. Neural Networks, 13{2), 2002,415-425.
  • [12] Cristianini, N., Shawe-Taylor, J.: An introduction to support vector machines, Cambridge, U. K.: Cambridge University Press, 2000.
  • [13] Burges, C. J. C: A tutorial on support vector machines for pattern recognition. Knowledge Discovery and Data Mining, 2, 1998, 955-974.
  • [14] Muller, K. R., Mika. S., Ratsch, G., Tsuda, K., Scholkopf. B.: An introduction to kernel-based learning algorithms, IEEE Trans. Neural Networks, 12(2), 2001, 181 -201.
  • [15] Blake, C. L., Merz, C. J.: UCI Repository of machine learning databases. Irvine, CA: University of California, Department of Information and Computer Science. [Online]. Available: http://www.ics.uci.edu/mleam/MLRepository.html
  • [16] Chang, C. C, Lin, C. J.: LIBSVM: a library for support vector machines, 2001. [Online]. Available: http://www.csie.ntu.edu.tw/cjUn/Iibsvm
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS2-0005-0070
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