Abstract
This work presents a novel feature fusion method for texture retrieval. Considering the advantages of both the spatial and frequency domain, we first carry on the experiments in spatial domain and frequency domain respectively. On one hand, sober and histogram feature are used to calculate the similarity. On the other hand, Fourier is applied to obtain the frequency feature. Then a feature fusion scheme is used to join the two features came from spatial and frequency domain. Experimental results on MIT texture database show that the proposed method is effective.
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Haralick, R.M., Shanmugam, K., Distein, I.: Textural features for image classification. IEEE Trans. on Systems, Man, and Cybernetics 3(6), 610–621 (1973)
Soh, K.S., Tsatsoulis, C.: Texture analysis of SAR sea ice imagery using gray level co-ocuurrence matrices. IEEE Trans. on Geoscience and Remote Sensing 37(2), 780–795 (1999); Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: A literature survey. ACM Computing Survey 34(4), 399–485 (2003)
Ulaby, F.T., Kouyate, F., Brisco, B., et al.: Textural information in SAR images. IEEE Transactions on Geoscience and Remote Sensing 24(2), 235–245 (1986); Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice-Hall, Upper Saddle River (2002)
Baraldi, A., Parmiggiani, F.: An investigation of the textual characteristics associated with gray level co-ocurrence matrix statistical parameters. IEEE Trans. on Geoscience and Remote Sensing 33(2), 293–304 (1995)
Hua, B., Ma, F.-L., Jiao, L.-C.: Research on computation of GLCM of image texture. Acta Electronica Sinica 34(1), 155–158 (2006)
Clausi, D.A., Jernigan, M.E.: A fast method to determine co-occurrence texture features. IEEE Trans. on Geoscience and Remote Sensing 36(1), 298–300 (1998)
Walker, R.F., Jackway, P.T., Longstaff, I.D.: Recent developments in the use of co-occurrence matrix for texture recognition. In: Proceedings of IEEE Conference on Digital Signal Processing, Santorini, Greece, vol. 1, pp. 63–65 (1997)
Kandaswamy, U., Adjeroh, D.A., Lee, M.C.: Efficient texture analysis of SAR imagery. IEEE Trans. on Geoscience and Remote Sensing 43(9), 2075–2083 (2005)
German, S., German, D.: Stochastic relaxation, gibbs distribution and Bayesian restoration of images. IEEE Trans. on Pattern Analysis and Machine Intelligence 6(6), 721–741 (1984)
McCormick, B.H., Jayaramamurthy, S.N.: Time series model for texture synthesis. International Journal of Computer Information Science 3(4), 329–343 (1974)
Ng, I., Tan, T., Kittler, J.: On local linear transform and Gabor filter representation of texture. In: Proceeding of the 11th IAPR Conference on Image, Speech and Signal Analysis, Hague, Netherlands, pp. 627–631 (1992)
Zhou, F., Feng, J., Shi, Q.: Image segmentation based on local fourier transform. In: Proceedings of International Conference on image Processing, Wuhan, China, pp. 610–613 (2001)
Dunn, D., Higgins, W.E., Wakeley, J.: Texture segmentation using 2-D Gabor elementary functions. IEEE Trans. on Pattern Analysis and Machine Intelligence 16(2), 130–149 (1994)
Pavlidis, T.: Structural Descriptions and Graph Grammars, pp. 86–103. Springer, Berlin (1980)
Soille, P.: Morphological Image Analysis: Principles and Applications, pp. 289–317. Springer Press, Berlin (2003)
Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Transactions in Information Theory IT-13, 21–27 (1967)
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Zhou, R. (2014). Spatial and Frequency Domain–Based Feature Fusion Method for Texture Retrieval. In: Pan, JS., Snasel, V., Corchado, E., Abraham, A., Wang, SL. (eds) Intelligent Data analysis and its Applications, Volume I. Advances in Intelligent Systems and Computing, vol 297. Springer, Cham. https://doi.org/10.1007/978-3-319-07776-5_27
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DOI: https://doi.org/10.1007/978-3-319-07776-5_27
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