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Bayesian texture classification and retrieval based on multiscale feature vector

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Çelik, Turgay and Tjahjadi, Tardi. (2011) Bayesian texture classification and retrieval based on multiscale feature vector. Pattern Recognition Letters, Vol.32 (No.2). pp. 159-167. ISSN 0167-8655

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Official URL: http://dx.doi.org/10.1016/j.patrec.2010.10.003

Abstract

This paper proposes a supervised multiscale Bayesian texture classifier. The classifier exploits the dual-tree complex wavelet transform (DT-CWT) to obtain complex-valued multiscale representations of training texture samples for each texture class. The high-pass subbands of DT-CWT decomposition of a texture image are used to form a multiscale feature vector representing magnitude and phase features. For computational efficiency, the dimensionality of feature vectors is reduced using principal component analysis (PCA). The class conditional probability density function of low-dimensional feature vectors for each texture class is then estimated by using Parzen-window estimate with identical Gaussian kernels and is used to represent the texture class. A query texture image is classified as the corresponding texture class with the highest a posteriori probability according to a Bayesian inferencing. The superior performance and robustness of the proposed classifier is demonstrated for classifying texture images from image databases. The proposed multiscale texture feature vector extracted from both magnitude and phase of DT-CWT subbands of a query image is also shown to be effective for texture retrieval.

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Item Type:	Journal Article
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Faculty of Science > Engineering
Library of Congress Subject Headings (LCSH):	Texture mapping, Wavelets (Mathematics)
Journal or Publication Title:	Pattern Recognition Letters
Publisher:	Elsevier BV * North Holland
ISSN:	0167-8655
Date:	15 January 2011
Volume:	Vol.32
Number:	No.2
Number of Pages:	9
Page Range:	pp. 159-167
Identification Number:	10.1016/j.patrec.2010.10.003
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Funder:	University of Warwick Vice Chancellor Scholarship
References:	Arivazhagan, S., Ganesan, L., 2003a. Texture classification using wavelet transform. Pattern Recognit. Letts. 24, 1513–1521. Arivazhagan, S., Ganesan, L., 2003b. Texture segmentation using wavelet transform. Pattern Recognit. Letts. 24, 3197–3203. Arivazhagan, S., Ganesan, L., Kumar, T. S., 2006a. Texture classification using ridgelet transform. Pattern Recognition Letters 27 (16), 1875–1883. Arivazhagan, S., Ganesan, L., Priyal, S., Dec 2006b. Texture classification using gabor wavelets based rotation invariant features. Pattern Recognit. Letts. 27 (16), 1976–1982. Brodatz, P., 1966. Textures: A Photographic Album for Artists and Designers. Dover, New York, USA. Celik, T., Tjahjadi, T., 2009. Multiscale texture classification using dual-tree complex wavelet transform. Pattern Recogn. Letts. 30 (3), 331–339. Daugman, J. G., 1980. Two-dimensional spectral analysis of cortical receptive field profiles. Vision Research 20 (10), 847–856. Do, M., Vetterli, M., Feb 2002. Wavelet-based texture retrieval using generalized gaussian density and kullback-leibler distance. IEEE Trans. Image Proc. 11 (2), 146–158. Duda, R. O., Hart, P. E., Stork, D. G., November 2000. Pattern Classification (2nd Edition), 2nd Edition. Wiley-Interscience. Faugeras, O., 1978. Texture analysis and classification using a human visual model. In: Proceedings of IEEE International Conference on Pattern Recognition. pp. 549–552. Fukunaga, K., Hayes, R., Apr 1989. The reduced parzen classifier. IEEE Transactions on Pattern Analysis and Machine Intelligence 11 (4), 423–425. Gonzalez, R. C., Woods, R. E., 2006. Digital Image Processing (3rd Edition). Prentice-Hall, Inc., Upper Saddle River, NJ, USA. Haralick, R. M., Shanmugam, K., Dinstein, I., Nov 1973. Textural features for image classification. IEEE Trans. Sys. Man Cybern. 3 (6), 610–621. Hiremath, P., Shivashankar, S., 2008. Wavelet based co-occurrence histogram features for texture classification with an appli- cation to script identification in a document image. Pattern Recognit. Letts. 29, 1182–1189. Jain, A., Farrokhnia, F., 1991. Unsupervised texture segmentation using gabor filters. Pattern Recognit. 24 (12), 1167–1186. Jain, A., Karu, K., Feb 1996. Learning texture-discrimination masks. IEEE Trans. Pattern Anal. Mach. Intell. 18 (2), 195–205. Kim, S., Kang, T., 2007. Texture classification and segmentation usingwavelet packet frame and gaussian mixture model. Pattern Recognit. 40, 1207–1221. Kingsbury, B. Y. N., 1999. Image processing with complex wavelets. Phil. Trans. Royal Society London A 357, 2543–2560. Kohavi, R., Provost, F., 1998. Glossary of Terms. Vol. 30. Kluwer Academic Publishers, Hingham, MA, USA. Kokare, M., Biswas, P., Chatterji, B., 2007. Texture image retrieval using rotated wavelet filters. Pattern Recognit. Letts. 28, 1240–1249. MITVisTex, 1998. Vision texture database. http://www.media.mit.edu/vismod/. Muneeswarana, K., Ganesanb, L., Arumugamc, S., Soundara, K., 2005. Texture classification with combined rotation and scale invariant wavelet features. Pattern Recognit. 38, 1495–1506. Smith, J. R., Chang, S.-F., February 1996. Tools and techniques for color image retrieval. In: IS&T/SPIE Symposium on Electronic Imaging: Science and Technology-Storage and Retrieval for Image and Video Databases IV. Vol. 2670. San Jose, CA. Wouwer, G., Scheunders, P., Dyck, D., Apr 1999. Statistical texture characterization from discrete wavelet representations. IEEE Trans. Image Proc. 8 (4), 592–598.
URI:	http://wrap.warwick.ac.uk/id/eprint/40486