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231223s2002 xx |||||o 00| ||eng c |
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|a 10.1109/83.982822
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|a eng
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| 100 |
1 |
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|a Do, Minh N
|e verfasserin
|4 aut
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| 245 |
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|a Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance
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|c 2002
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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| 500 |
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|a Date Completed 20.05.2010
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|a Date Revised 04.02.2008
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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|a We present a statistical view of the texture retrieval problem by combining the two related tasks, namely feature extraction (FE) and similarity measurement (SM), into a joint modeling and classification scheme. We show that using a consistent estimator of texture model parameters for the FE step followed by computing the Kullback-Leibler distance (KLD) between estimated models for the SM step is asymptotically optimal in term of retrieval error probability. The statistical scheme leads to a new wavelet-based texture retrieval method that is based on the accurate modeling of the marginal distribution of wavelet coefficients using generalized Gaussian density (GGD) and on the existence a closed form for the KLD between GGDs. The proposed method provides greater accuracy and flexibility in capturing texture information, while its simplified form has a close resemblance with the existing methods which uses energy distribution in the frequency domain to identify textures. Experimental results on a database of 640 texture images indicate that the new method significantly improves retrieval rates, e.g., from 65% to 77%, compared with traditional approaches, while it retains comparable levels of computational complexity
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|a Journal Article
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|a Vetterli, Martin
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 11(2002), 2 vom: 15., Seite 146-58
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|x 1941-0042
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| 773 |
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|g volume:11
|g year:2002
|g number:2
|g day:15
|g pages:146-58
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|u http://dx.doi.org/10.1109/83.982822
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