Invariant content-based image retrieval by wavelet energy signatures
C. Pun. Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP
'03). 2003 IEEE International Conference on, 3, Seite III-565-8. IEEE Computer Society, (2003)
DOI: 10.1109/ICASSP.2003.1199537
Zusammenfassung
An effective rotation and scale invariant log-polar wavelet texture
feature for image retrieval was proposed. The feature extraction
process involves a log-polar transform followed by an adaptive row
shift invariant wavelet packet transform. The log-polar transform
converts a given image into a rotation and scale 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
and scale 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 log-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/spl middot/log n)
complexity. Experimental results show that this rotation and scale
invariant texture feature is effective and outperforms the traditional
wavelet packet signatures.
%0 Conference Paper
%1 Pun2003
%A Pun, Chi-Man
%B Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP
'03). 2003 IEEE International Conference on
%D 2003
%I IEEE Computer Society
%K adaptive coefficient complexity, content-based cost dimensionality energy extraction, feature feature, function, image information invariant log-polar packet reduction, retrieval, rotation row scale shift signatures, subbands, texture texture, transform transform, transforms vector, wavelet
%P III-565-8
%R 10.1109/ICASSP.2003.1199537
%T Invariant content-based image retrieval by wavelet energy signatures
%V 3
%X An effective rotation and scale invariant log-polar wavelet texture
feature for image retrieval was proposed. The feature extraction
process involves a log-polar transform followed by an adaptive row
shift invariant wavelet packet transform. The log-polar transform
converts a given image into a rotation and scale 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
and scale 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 log-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/spl middot/log n)
complexity. Experimental results show that this rotation and scale
invariant texture feature is effective and outperforms the traditional
wavelet packet signatures.
@inproceedings{Pun2003,
abstract = {An effective rotation and scale invariant log-polar wavelet texture
feature for image retrieval was proposed. The feature extraction
process involves a log-polar transform followed by an adaptive row
shift invariant wavelet packet transform. The log-polar transform
converts a given image into a rotation and scale 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
and scale 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 log-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/spl middot/log n)
complexity. Experimental results show that this rotation and scale
invariant texture feature is effective and outperforms the traditional
wavelet packet signatures.},
added-at = {2011-03-27T19:35:34.000+0200},
author = {Pun, Chi-Man},
biburl = {https://www.bibsonomy.org/bibtex/2f76a446a65b1cd4385ad8e632ea7d54e/cocus},
booktitle = {Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP
'03). 2003 IEEE International Conference on},
booktitleaddon = {Apr 6--10, 2003},
doi = {10.1109/ICASSP.2003.1199537},
file = {:./01199537.pdf:PDF},
interhash = {0a14960c6c751981369b67a14140ca45},
intrahash = {f76a446a65b1cd4385ad8e632ea7d54e},
issn = {1520-6149},
keywords = {adaptive coefficient complexity, content-based cost dimensionality energy extraction, feature feature, function, image information invariant log-polar packet reduction, retrieval, rotation row scale shift signatures, subbands, texture texture, transform transform, transforms vector, wavelet},
location = {#ieeeaddr#},
pages = {III-565-8},
publisher = {{IEEE} Computer Society},
timestamp = {2011-03-27T19:35:42.000+0200},
title = {Invariant content-based image retrieval by wavelet energy signatures},
volume = 3,
year = 2003
}