Abstract
Wavelet Domain Hidden Markov Model (WD HMM), in particular Hidden Markov Tree (HMT), has recently been proposed and applied to gray level image analysis. In this paper, color texture analysis using WD HMM is studied. In order to combine color and texture information to one single model, we extend WD HMM by grouping the wavelet coefficients from different color planes to one vector. The grouping way is chose according to a tradeoff between computation complexity and effectiveness. Besides, we propose Multivariate Gaussian Mixture Model (MGMM) to approximate the marginal distribution of wavelet coefficient vectors and to capture the interactions of different color planes. By employing our proposed approach, we can improve the performance of WD HMM on color texture classification. The experiment shows that our proposed WD HMM provides an 85% percentage of correct classifications (PCC) on 68 color images from an Oulu Texture Database and outperforms other methods.
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References
Crouse, M.S., Nowak, R.D., Baraniuk, R.G.: Wavelet-based statistical signal processing using hidden Markov model. IEEE Trans. Signal Proc. 46(4), 886–902 (1998)
University of Oulu texture database, available at http://www.outex.oulu.fi/outex.php
Van de Wouwer, G., Livens, S., Scheunders, P., Van Dyck, D.: Color texture classification by wavelet energy correlation signatures. Pattern Recognition, Special issue on Color and Texture Analysis (1998)
Ohta, Y.: Knowledge based interpretation of outdoor natural scenes. Pitman Publishing, London (1985)
Fan, G., Xia, X.G.: Image de-noising Using Local Contextual Hidden Markov Model in the Wavelet-Domain. IEEE Signal Processing Lett. 8, 125–128 (2001)
Fan, G., Xia, X.G.: Maximum likelihood texture analysis and classification using wavelet-domain hidden Markov models. In: Proc. 34th Asilomar Conf. Signals, Systems, and Computers Pacific Grove, CA (October 2000)
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© 2004 Springer-Verlag Berlin Heidelberg
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Siyi, D., Jie, Y., Qing, X. (2004). Color Texture Analysis Using Wavelet-Based Hidden Markov Model. In: Webb, G.I., Yu, X. (eds) AI 2004: Advances in Artificial Intelligence. AI 2004. Lecture Notes in Computer Science(), vol 3339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30549-1_99
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DOI: https://doi.org/10.1007/978-3-540-30549-1_99
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24059-4
Online ISBN: 978-3-540-30549-1
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