這篇論文對各種紋理特徵的擷取方法所獲得的紋理特徵，針對其特性在支援向量機和自我組織映射圖上做效能比較。特徵的擷取方法使用Gabor轉換和四種不同基底的小波轉換，並在支援向量機和自我組織映射圖上分析其特性，更甚者，從不同的角度來看，特徵值經過正規化後，在不同的分類器上會對其特性有何影響。
實驗使用的資料庫是從Brodatz資料庫中挑選盡可能是有一般規律性的九十六類，每一類為十六張，實驗中每一類的一半作為分類器學習之用，另一半則為測試之用，並將其所有效能測試結果列於第五章的實驗結果之中。

Experimental results of using various texture features based on Support Vector Machine (SVM) and Self-Organizing Map (SOM) are reported in this thesis. For classification, the texture features are derived from Gabor and four wavelet transforms (9/7, 5/3, Daubechies’ four, and Haar transforms). Then, the performance of various texture features will be evaluated by SVM and SOM. Moreover, a comparison of SVM and SOM for texture classification will be presented and illustrated in the final experimental results. Besides, the texture features for SVM and SOM have worked from a lightly different viewpoint; the training data with scaling or non-scaling process may heavily affect the classification rate.
Our database consists of 96 classes as homogeneous as possible (1536 images of size 128×128) from Brodatz’s album. So the performance evaluation of various texture features with SVM and SOM will be tested on our database and be reported in the experimental results.

[Ant92] M. Antonini, M. Barlaud, P. Mathieu, and I. Daubechies, “Image Coding Using Wavelet Transforms,” IEEE Trans. on Image Processing, vol. 1, pp. 205-220, 1992.
[Che05] C.C. Chen and H.T. Chu, “Similarity Measurement Between Images,” IEEE Conf. on Computer Software and Applications (Compsac’05), vol. 2, pp. 41-42, 2005.
[Che93] C.C. Chen and C.L. Huang, “Markov Random Fields for Texuture Classification,” Pattern Recognition Letter, vol. 14, pp. 907-914, 1993.
[Che99] C.C. Chen and C.C. Chen, “Filtering Methods for Texture Analysis,” Pattern Recognition Letter, vol. 20, pp. 783-790, 1999.
[Cor95] C. Cortes and V. Vapnik, Support-vector network. Machine Learning No. 20, pp. 273-297, 1995.
[Dau88] I. Daubechies, “Orthonormal Bases of Compactly Supported Wavelets,” Communications on Pure and Applied Mathematics, vol. 41, pp. 909-996, 1988.
[Gab46] D. Gabor. Theory of communication. J. Inst. Elec. Eng., vol. 93(III), pp. 429-457, 1946.
[Gou84] P. Goupillaud, A. Grossmann and J. Morlet, “Cycle-octave and Related Transforms in Seismic Signal Analysis,” Geoexploration, vol. 23, pp. 85-102, 1984-1985.
[Hsu02] C.W. Hsu and C.J. Lin, “A comparison of methods for multiclass support vector machines,” IEEE Trans. Neural Netw., vol. 13, pp. 415-425, 2002.
[ISO00] ISO/IEC 15444-1, Information Technology JPEG2000 image coding system, 2000.
[Jai91] A. K. Jain and F. Farrokinia, “Unsupervised Texture Segmentation Using Gabor Filters,” Pattern Recognition, vol. 24, pp. 1167-1186, 1991.
[Kee01] S. Keerthi, S. Shevade, C. Bhattacharyya and K. Murthy, “Improvements to Platt’s SMO algorithm for SVM classifier design,” Neural Computation, vol. 13, pp. 637-649, 2001.
[Koh01] T. Kohonen, Self-Organizing Maps, 3rd Extended Edition, Springer, Berlin, 2001.
[Koh82] T. Kohonen, “Self-Organizing Formation of Topologically Correct Feature Maps,” Biological Cybernetics, vol. 43, pp. 59-69, 1982.
[Koh90] T. Kohonen, “The Self-Organizing Map,” IEEE Proceedings, vol. 78, pp. 1464-1480, 1990.
[Koh92] T. Kohonen. “New developments of learning vector quantization and the self-organizing map,” in Symposium on Neural Networks; Alliances and Perspectives in Senri (SYNAPSE 1992), Osaka, Japan, 1992.
[Lan04] X. Lan, N. Zheng, Y. Wu, Y. Liu, Z. Liu and K. Mei, “Highly Efficient and Parallel VLSI Architecture Design for JPEG2000 of 2D Discrete Wavelet Transform,” Journal of Xian Jiaotong University, vol. 38, pp. 149-153, 2004.
[Mar80] S. Marcelja, “Mathematical description of the responses of simple cortical cells,” J, Opt. Soc. Amer., vol. 70, pp. 1297-1300, 1980.
[Man00] B.S. Manjunath, P. Wu, S. Newsam and H.D. Shin, "A texture descriptor for browsing and similarity retrieval," Journal of Signal Processing: Image Communication, vol. 16, pp. 33-43, 2000
[Man96a] B.S. Manjunath and W.Y. Ma, “Texture features for browsing and retrieval of image data,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 18, pp. 837-842, 1996.
[Man96b] P. Mangiameli, S.K. Chen and D. West, “A Comparison of SOM Neural Network and Hierarchical Clustering Methods,” European J. Operational Research, vol. 93, pp. 402-417, 1996.
[Nou96] M. Nour and G. Madey, ”Heuristic and Optimization Approaches to Extending the Kohonen Self-Organizing Algorithm,” European J. Operational Research, vol. 93, pp. 428-448, 1996.
[Pla99] J. Platt, Fast training of support vector machines using sequential minimal optimization, in: B. Scholkopf, C. Burges and A. Smola, Advances in Kernel Methods - Support Vector Learning, MIT Press, Cambridge, 1999.
[Rao98] R.M. Rao and A.S. Bopardiskar, “Wavelet Transforms: Introduction to Theory and Applications,” 1998.
[Sch99] B. Scholkopf, C. Burges and A. Smola, Advances in Kernel Methods - Support Vector Learning, MIT Press, Cambridge, 1999.
[Su99] M.C. Su, T.K. Liu, and H.T. Chang, “An efficient initialization scheme for the self-organizing feature map algorithm,” IEEE Int. Joint Conference on Neural Networks, pp. 1906-1910, 1999.
[Tau02] D.S. Taubman, M.W. Marcellin, JPEG2000: Image Compression Fundamentals, Standards and Practice, Kluwer Academic Publishers, Boston, 2002.
[Vap95] V. Vapnik, The Nature of Statistical Learning Theory, 1995.
[Ves00] J. Vesanto and E. Alhoniemi, “Clustering of the Self-Organizing Map,” IEEE Trans. on Neural Networks, vol. 11, pp. 586-600, 2000.
[Zha02] J. Zhang and T. Tan, “Brief Review of Invariant Texture Analysis Methods,” Pattern Recognition, vol. 35, pp. 735-747, 2002.