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Log-polar wavelet energy signatures for rotation and scale invariant texture classification
Pun C.-M.1; Lee M.-C.2
2002-09-21
Source PublicationIEEE Transactions on Pattern Analysis and Machine Intelligence
ISSN1628828
Volume25Issue:5Pages:590
Abstract

Classification of texture images, especially those with different orientation and scale changes, is a challenging and important problem in Image analysis and classification. This paper proposes an effective scheme for rotation and scale Invariant texture classification using log-polar wavelet signatures. The rotation and scale Invariant feature extraction for a given image involves applying a log-polar transform to eliminate the rotation and scale effects, but at same time produce a row shifted log-polar image, which is then passed to an adaptive row shift invariant wavelet packet transform to eliminate the row shift effects. So, the output wavelet coefficients are rotation and scale invariant. The adaptive row shift invariant wavelet packet transform is quite efficient with only O(n·log n) complexity. A feature vector of the most dominant log-polar wavelet energy signatures extracted from each subband of wavelet coefficients is constructed for rotation and scale invariant texture classification. In the experiments, we employed a Mahalanobis classifier to classify a set of 25 distinct natural textures selected from the Brodatz album. The experimental results, based on different testing data sets for images with different orientations and scales, show that the proposed classification scheme using log-polar wavelet signatures outperforms two other texture classification methods, its overall accuracy rate for joint rotation and scale invariance being 90.8 percent, demonstrating that the extracted energy signatures are effective rotation and scale Invariant features. Concerning its robustness to noise, the classification scheme also performs better than the other methods.

KeywordLog-polar Transform Rotation And Scale Invariance Shift Invariant Wavelet Packet Transform Texture Classification
DOI10.1109/TPAMI.2003.1195993
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000182342300005
PublisherIEEE COMPUTER SOC
The Source to ArticleScopus
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被引频次[WOS]:173   [WOS记录]     [WOS相关记录]
Document TypeJournal article
专题DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorLee M.-C.
Affiliation1.Faculty of Science and Technology, University of Macau, Macau S.A.R.
2.Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong S.A.R.
First Author AffilicationFaculty of Science and Technology
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Pun C.-M.,Lee M.-C.. Log-polar wavelet energy signatures for rotation and scale invariant texture classification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,25(5):590.
APA Pun C.-M.,&Lee M.-C..(2002).Log-polar wavelet energy signatures for rotation and scale invariant texture classification.IEEE Transactions on Pattern Analysis and Machine Intelligence,25(5),590.
MLA Pun C.-M.,et al."Log-polar wavelet energy signatures for rotation and scale invariant texture classification".IEEE Transactions on Pattern Analysis and Machine Intelligence 25.5(2002):590.
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