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. doi:10.1016/j.patrec.2010.10.003

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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.

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
Official Date:	15 January 2011
Dates:	Date Event 15 January 2011 Published
Volume:	Vol.32
Number:	No.2
Number of Pages:	9
Page Range:	pp. 159-167
DOI:	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

Data sourced from Thomson Reuters' Web of Knowledge

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