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Rotation-invariant texture feature for image retrieval
Chi-Man Pun
2003
Source PublicationComputer Vision and Image Understanding
ISSN1077-3142
Volume89Issue:1Pages:24-43
Abstract

An effective rotation-invariant polar-wavelet texture feature for image retrieval was proposed. The feature extraction process involves a polar transform followed by an adaptive row shift invariant wavelet packet transform. The polar transform converts a given image into a rotation-invariant but row-shifted image, which is then passed to the adaptive row shift invariant wavelet packet transform to generate adaptively some subbands of rotation-invariant wavelet coefficients with respect to an information cost function. An energy signature is computed for each subband of these wavelet coefficients. In order to reduce feature dimensionality, only the most dominant polar-wavelet energy signatures are selected as feature vector for image retrieval. The whole feature extraction process is quite efficient and involves only O(n · log n) complexity. Experimental results show that this rotation-invariant texture feature is effective and outperforms the other image retrieval algorithms.

KeywordImage Retrieval Rotation Invariance Shift Invariance Wavelet Packet Transform
DOI10.1016/S1077-3142(03)00012-2
URLView the original
Language英语
The Source to ArticleScopus
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Cited Times [WOS]:23   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
AffiliationDepartment of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau
First Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Chi-Man Pun. Rotation-invariant texture feature for image retrieval[J]. Computer Vision and Image Understanding,2003,89(1):24-43.
APA Chi-Man Pun.(2003).Rotation-invariant texture feature for image retrieval.Computer Vision and Image Understanding,89(1),24-43.
MLA Chi-Man Pun."Rotation-invariant texture feature for image retrieval".Computer Vision and Image Understanding 89.1(2003):24-43.
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