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Data Mining and Its Use in Texture Analysis

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper deals with an idea of the use of data mining approach in texture analysis. A new method based on association rules is proposed. This method utilizes the multiresolution analysis of an analyzed image texture and works at the texture primitive level. Within this method, a technique for feature vector construction without any a priori knowledge of textures different from the analyzed one is presented. The contribution also contains some results of texture image segmentation experiments.
Wydawca
Rocznik
Strony
173--186
Opis fizyczny
Bibliogr. 18 poz., fot., tab.
Twórcy
autor
  • Faculty of Information Technology, Brno University of Technology, Bozetechova 2, Brno 61266, Czech Republic
autor
  • Faculty of Information Technology, Brno University of Technology, Bozetechova 2, Brno 61266, Czech Republic
Bibliografia
  • [1] Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P.: The KDD Process for Extracting Useful Knowledge front Volumes of Data. In: Communications of the ACM. Data Mining. November 1996. Vol.39, Number 1I, pp.27-34.
  • [2] Zaiane. O. R. et al.: MultiMediaMiner: A system prototype for multimedia data mining. In. Proc. 1998 ACM SIGMOD conf. on Management of Data (SIGMOD'98). Seattle, WA, 1998, pp. 581-583.
  • [3] Zaiane. O. R., Han, J., Zhu, H.: Mining Recurrent Items in Multimedia with Progressive Resolution Refinement. In: Proc. 2000 Int. Conf. on Data Engineering (ICDE’00), San Diego, CA, 2000, pp. 461-470.
  • [4] Han. J., Kamber. M.: Data Mining Techniques And Concepts. Morgan Kaufmann Publishers, 2000.
  • [5] Rushing, J. A., Ranganath, H. S., Hinke, T. H., Graves, S. J.: Using Association Rules as Texture Features. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No 8, 2001, pp. 845-858.
  • [6] Tuceryan, M., Jain. A. K.: Texture Analysis. In: Handbook of Pattern Recognition and Computer Vision (2nd edition), World Scientific Publishing Co., 1998, pp. 207-248.
  • [7] Haralick, R. M., Shanmugam, K., Dinstein, I.: Textural Features for Image Classification. IEEE Transactions on Systems. Man and Cybernetics, Vol. 3, 1973, pp. 610-621.
  • [8] Laws, K. I.: Textured Image Segmentation. Ph.D. thesis, University of Southern California. 1980.
  • [9] Manjunath. B. S., Ma, W. Y.: Texture Features for Browsing and Retrieval of Image Data. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 18, 1996, pp. 837-842.
  • [10] Wu, P., Manjunath, B. S., Newsam, S., Shin, H. D.: A Texture Descriptor for Browsing and Similarity Retrieval. Journal of Signal Processing: Image Communication. Vol. 16. Issue 1-2, 2000, pp. 33-43.
  • [11] Smith, J. R., Chang, S.: Transform Features For Texture Classification and Discrimination in Large Image Databases. Proceedings of the IEEE International Conference on Image Processing, 1994, pp. 407-411.
  • [12] Zucker. S. W.: Toward a Model of Texture. Computer Graphics and Image Processing, 5. pp. 190-202, 1976.
  • [13] Fu. K. S.: Syntactic Pattern Recognition and Applications. Prentice Hall. New Jersey, 1982.
  • [14] Fournier. A.: Wavelets and their Applications in Computer Graphics. SIGGRAPH'95 Course Notes, 1995.
  • [15] Hartigan. J. A., Wong. M. A.: A к-means Clustering Algorithm. Applied Statistics, 28. 1979. pp. 100-108.
  • [16] Rosenfeld, A., Pfaltz, J. L.: Sequential Operations in Digital Image Processing. Journal of the Association for Computing Machinery, Vol. 13, 1966, pp. 471-494.
  • [17] Agrawal, R., Imielinski. Т., Swami, A.: Mining Association Rules Between Sets of Items in Large Databases. Proceedings of the ACM S1GM0D International Conference on Management of Data, Washington, USA, 1993, pp.207-216.
  • [18] Brodatz. P.: Textures: A Photographic Album for Artists and Designers. New York, Dover Publication, 1966.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS2-0005-0034
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