Abstract
This paper presents a new feature extraction method in dual-tree complex wavelet transform domain. Given an input image, we obtain all highpass directional subimages and a set of pyramid lowpass subimages with different resolutions by applying DTCWT decomposition. After that, generalized Gamma density \((\hbox {G}\Gamma \hbox {D})\) models and local binary pattern are utilized respectively to characterize features of both highpass and lowpass subimages. The two kinds of features are combined for texture classification, and the experimental results on datasets Brodatz, Outex and UMD demonstrate that our proposed method can achieve superior classification accuracy than other state-of-the-art methods.
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References
Allili, M. (2012). Wavelet modeling using finite mixtures of generalized Gaussian distributions: Application to texture discrimination and retrieval. IEEE Transactions on Image Processing, 21(4), 1452–1464.
Bashar, M. K., & Ohnishi, N. (2011). Wavelet-based salient energy points for unsupervised texture segmentation. International Journal of Pattern Recognition and Artificial Intelligence, 19(3), 429–458.
Çelik, T., & Tjahjadi, T. (2011). Multiscale texture classification and retrieval based on magnitude and phase features of complex wavelet subbands. Computers & Electrical Engineering, 37(5), 729–743.
Choy, S. K., & Tong, C. (2010). Statistical wavelet subband characterization based on generalized Gamma density and its application in texture retrieval. IEEE Transactions on Image Processing, 19(2), 281–289.
Do, M. N., & Vetterli, M. (2002). Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance. IEEE Transactions on Image Processing, 11(2), 146–158.
Guo, Z. H., Zhang, L., & Zhang, D. (2010). A completed modeling of local binary pattern operator for texture classification. IEEE Transactions on Image Processing, 19(6), 1657–1663.
Heikkila, M., Pietikainen, M., & Schmid, C. (2009). Description of interest regions with local binary patterns. Pattern Recognition, 42(3), 425–436.
Kokare, M., Biswas, P. K., & Chatterji, B. N. (2005). Texture image retrieval using new rotated complex wavelet filters. IEEE Transactions on Systems, Man, and Cybernetics, 35(6), 1168–1178.
Koleini, M., Ahmadzadeh, M. R., & Sadri, S. (2014). A new efficient method to characterize dynamic textures based on a two-phase texture and dynamism analysis. Pattern Recognition Letters, 45(8), 217–225.
Krylov, Vladimir A., Moser, Gabriele, Serpico, Sebastiano B., & Zerubia, Josiane. (2013). On the method of logarithmic cumulants for parametric probability density function estimation. IEEE Transactions on Image Processing, 22(10), 3791–3806.
Kwitt, R., & Uhl, A. (2010). Lightweight probabilistic texture retrieval. IEEE Transactions on Image Processing, 19(1), 241–253.
Liao, S., Law, M. W. K., & Chung, A. C. S. (2009). Dominant local binary patterns for texture classification. IEEE Transactions on Image Processing, 18(5), 1107–1118.
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971–987.
Qian, X., Hua, X. S., Chen, P., & Ke, L. (2011). PLBP: An effective local binary patterns texture descriptor with pyramid representation. Pattern Recognition, 44(10–11), 2502–2515.
Selesnick, I. W., Baraniuk, R. G., & Kingsbury, N. C. (2005). The dual tree complex wavelet transform. IEEE Signal Processing Magazine, 22(6), 123–151.
Shrivastava, N., & Tyagi, V. (2013). An effective scheme for image texture classification based on binary local structure pattern. Visual Computer, 30(11), 1223–1232.
Ves, E. D., Acevedo, D., Ruedin, A., & Benavent, X. (2014). A statistical model for magnitudes and angles of wavelet frame coefficients and its application to texture retrieval. Pattern Recognition, 47(9), 2925–2939.
Vo, A., & Oraintara, S. (2010). A study of relative phase in complex wavelet domain: Property, statistics and applications in texture image retrieval and segmentation. Signal Processing: Image Communication, 25(1), 28–46.
Wouwer, G. V., Scheunders, P., & Dyck, D. V. (1999). Statistical texture characterization from discrete wavelet representations. IEEE Transactions on Image Processing, 8(4), 592–598.
Zhang, W., Shan, S., Gao, W., Chen, X., & Zhang, H. (2005). Local Gabor binary pattern histogram sequence (LGBPHS): A novel non-statistical model for face representation and recognition. Proceedings of Tenth IEEE International Conference on Computer Vision, pp. 786–791.
Acknowledgements
We are grateful to the referees for their valuable comments. This work is financially supported by the National Natural Science Foundation of China (61662048, 61363050, 61272077, 61563037).
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Yang, P., Yang, G. Statistical model and local binary pattern based texture feature extraction in dual-tree complex wavelet transform domain. Multidim Syst Sign Process 29, 851–865 (2018). https://doi.org/10.1007/s11045-017-0474-z
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DOI: https://doi.org/10.1007/s11045-017-0474-z