DocumentCode :
2172823
Title :
How to perform texture recognition from stochastic modeling in the wavelet domain
Author :
Atto, Abdourrahmane M. ; Berthoumieu, Yannick
Author_Institution :
Lab. IMS, Univ. de Bordeaux, Talence, France
fYear :
2011
fDate :
22-27 May 2011
Firstpage :
4320
Lastpage :
4323
Abstract :
The paper addresses content-based image retrieval from texture data bases, by using stochastic modeling in the wavelet domain. It pro poses an analysis of the key parameters involved in such a content based texture retrieval. These parameters are the wavelet order and the goodness-of-fit measure used to select the best family of distributions for modeling the subband wavelet coefficients. It is shown that taking suitable parameters into consideration makes it possible to attain high retrieval rates in content-based texture retrieval.
Keywords :
content-based retrieval; image recognition; image retrieval; image texture; stochastic processes; wavelet transforms; content based image retrieval; goodness-of-fit measure; stochastic modeling; subband wavelet coefficients; texture databases; texture recognition; wavelet domain; wavelet order; Computational modeling; Databases; Optical fibers; Stochastic processes; Wavelet packets; Similarity; Stochastic modeling; Texture; Wavelets;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on
Conference_Location :
Prague
ISSN :
1520-6149
Print_ISBN :
978-1-4577-0538-0
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2011.5947309
Filename :
5947309
Link To Document :
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