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Classifying rotated textures using wavelet packet signatures
Lee M.-C.; Pun C.-M.
2000
Conference Name2000 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, PROCEEDINGS
Source PublicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume4
Pages2215-2218
Conference DateJUN 05-09, 2000
Conference PlaceISTANBUL, TURKEY
Abstract

This paper proposes a novel approach to the classification of rotated texture images. The proposed classification method involves decomposing a texture image with a family of real orthonnormal wavelet bases for different levels, computing the wavelet packet coefficients, and computing the energy signatures using the wavelet packet coefficients. Such energy signatures are sorted and used as a feature for texture image classification. We employ a Mahalanobis distance classifier to classify a set of twenty distinct natural textures selected from the Brodatz album. Experimental results, based on a large sample data set having different orientations, show that the proposed method outperforms other methods which may perform well in the classification of texture images having the same orientation.

KeywordClassification
DOI10.1109/ICASSP.2000.859278
URLView the original
Language英语
WOS Research AreaAcoustics ; Computer Science ; Engineering
WOS SubjectAcoustics ; Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS IDWOS:000088999500555
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Citation statistics
Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
AffiliationChinese University of Hong Kong
Recommended Citation
GB/T 7714
Lee M.-C.,Pun C.-M.. Classifying rotated textures using wavelet packet signatures[C],2000:2215-2218.
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