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Texture classification using dominant wavelet packet energy features
Lee M.-C.; Pun C.-M.
2000
Conference Name4th IEEE Southwest Symposium on Image Analysis and Interpretation
Source PublicationProceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation
Volume2000-January
Pages301-304
Conference Date2-4 April 2000
Conference PlaceAustin, TX, USA, USA
Abstract

This paper proposes a high performance texture classification method using dominant energy features from wavelet packet decomposition. We decompose the texture images with a family of real orthonormal wavelet bases and compute the energy signatures using the wavelet packet coefficients. Then we select a few of the most dominant energy values as features and employ a Mahalanobis distance classifier to classify a set of distinct natural textures selected from the Brodatz album. In our experiments, the proposed method employed a reduced feature set and involved less computation in classification time while still archiving high accuracy rate (94.8%) for classifying twenty classes of natural texture images.

KeywordClassification Tree Analysis Electronic Mail Filters Frequency Image Texture Analysis Moon Power Engineering And Energy Read Only Memory Wavelet Packets Wavelet Transforms
DOI10.1109/IAI.2000.839620
URLView the original
Language英语
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Citation statistics
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.. Texture classification using dominant wavelet packet energy features[C],2000:301-304.
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