Czasopismo
2012
|
Vol. 114, nr 2
|
203-220
Tytuł artykułu
Autorzy
Wybrane pełne teksty z tego czasopisma
Warianty tytułu
Języki publikacji
Abstrakty
The classification of texture images, especially those with spatial rotation and region shift, is a challenge and important problem in image analysis and classification. This paper proposes a novel algorithm design, an ellipse invariant algorithm, to improve the capability of texture classification for spatial rotation and region shift. The principle of an ellipse invariant algorithm is to use a minimum ellipse to enclose specific representative pixels extracted by the subtracting clustering method. After translating the coordinates, the ellipse in the rotated texture would be formulated as the ellipse in original texture. Also in this paper a hybrid texture filter is proposed. In the hybrid texture filter the scheme of texture feature extraction include Gabor wavelet, neighboring grey level dependence matrix and the ellipse invariant algorithm. Support vector machines (SVMs) are introduced as the classifier. The proposed hybrid texture filter can classify both the stochastic textures and structural textures. Experimental results reveal that this proposed algorithm outperforms existing design algorithms.
Czasopismo
Rocznik
Tom
Strony
203-220
Opis fizyczny
Bibliogr. 27 poz., rys., tab., wykr.
Twórcy
autor
autor
- Department of Computer Science and Information Engineering Chaoyang University of Technology Wufong, Taichung 41349, Taiwan, ccyao@cyut.edu.tw
Bibliografia
- [1] Chellappa, R., Kashyap, R. L.: Texture synthesis using 2-D noncausal autoregressive models, IEEE Trans. Acoust., Speech, Signal Process., 33(1), 2003, 194-203.
- [2] Fan, G., Xia, X.-G.: Wavelet-based texture analysis and synthesis using hidden Markov models, IEEE Trans. Circuits Syst., 50(1), 2003, 106-120.
- [3] Dasgupta, N., Carin, L.: Texture analysis with variational Hidden Markov trees, IEEE Trans. on Signal Processing, 54(6), 2006, 2352-2356.
- [4] Shou, Y. -W., Lin, C. -T.: Image descreening by GA-CNN-Based texture classification, IEEE Trans. on Circuits and Systems - I: Regular Papers, 51(11), 2004, 2287-2299.
- [5] Kim, K. -I., Jung, K., Park, S. -H., Kim, H. -J.: Support vector machines for texture classification, IEEE Trans. on Pattern Analysis and Machine Intelligence, 24(11), 2002, 1542-1550.
- [6] Selvan, S., Ramakrishnan, S.: SVD-based modeling for image texture classification using wavelet transformation, IEEE Trans. on Image Processing, 16(11), 2007, 2688-2696.
- [7] Kim, S. -C., Kang, T. -J.: Texture classification and segmentation using wavelet packet frame and Gaussian mixture model, Pattern Recognition, 40, 2007, 1207-1221.
- [8] Leon, S. J.: Linear algebra with applications, Macmillan, New York, 1990.
- [9] Mellor, M., Hong, B.-W., Brady, M.: Locally rotation, contrast, and scale invariant descriptors for texture analysis, IEEE Trans. on Pattern Analysis and Machine Intelligence, 30(1), 2007, 52-61.
- [10] Xia, Y., Feng, D., Zhao, R.: Adaptive segmentation of textured images by using the coupled Markov random field model, IEEE Trans. on Image Processing, 15(11), 2006, 3559-3566.
- [11] Kaplan, L. M.: Extended fractal analysis for texture classification and segmentation, IEEE Trans. on Image Processing, 8(11), 1999, 1572-1585.
- [12] Liu, D., Lam, K. M., Shen, L. S.: Optimal sampling of Gabor features for face recognition, Pattern Recognition Lett., 25(2), 2004, 267-276.
- [13] Choi, W. -P., Tse, S. -H.,Wong, K. -W., Lam, K. -M.: Simplified Gabor wavelets for human face recognition, Pattern Recognition, 25, 2008, 1186-1199.
- [14] Filho, O. C. R., Treitz, P. M., Soulis, E. D., Howarth, P. J., Kouwen, N.: Texture processing of synthetic aperture radar data using second-order spatial statistics, Computers and Geosciences, 22(1), 1996, 27-34.
- [15] Sun, C., Wee, W. G.: Neighboring gray level dependence matrix for texture classification, Computer Vision, Graphics and Image Processing, 23, 1982, 341-352.
- [16] Schölkopf, B., Smola, A.J.: Learning with kernels: Support vector machines, regularization, optimization, and beyond, Cambridge, Mass: MIT Press, London, 2002.
- [17] Vapnik, V. N.,: Statistical learning theory, Wiley, New York, 1998.
- [18] Yao, C.-C., Yu, P.-T.: Fuzzy regression by asymmetric support vector machines, Applied Mathematics and Computation, 182, 2006, 175-193.
- [19] Chiu, S. L.: A cluster estimation method with extension to fuzzy model identification, Proc. IEEE International Conf. on Fuzzy Systems, 2(3), 1994, 1240-1245.
- [20] Hsu, C. -W., Lin, C. -J.: A comparison of methods for multiclass support vector machines, IEEE Trans. On Neural Networks, 13(2), 2002, 415-425.
- [21] Chapelle, O., Haffner, P., Vapnik, V. N.: Support vector machines for histogram-based image classification, IEEE Trans. on Neural Network, 10(5), 1999, 1055-1064.
- [22] Guo, G., Li, S.Z., Chan, K.L.: Support vector machines for face recognition, Image and Vision Computing, 19, 2001, 631-638.
- [23] Avci, E.: An expert system based on wavelet neural network-adaptive norm entropy for scale invariant texture classification, Expert System with Applications, 32(3), 2007, 919-926.
- [24] Brodatz, P.: Textures: A Photographic Album for Artists and Designers, Dover, New York, 1999.
- [25] Stone, M.: Cross-validatory choice and assessment of statistical predictions, Journal of the Royal Statistical Society, 36, 1974, 111-147.
- [26] Murphy, P. M., Aha, D. W.: UCI repository of machine learning databases, 1992, www.ics.uci.edu/ mlearn/MLRepository.html.
- [27] Ro, Y. M., Kim,M., Kang, H. K., Manjunath, B. S., Kim, J.: MPEG-7 homogeneous texture descriptor, ETRI Journal, 23(2), 2001, 41-51.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS8-0028-0005