Volume 6 Number 6 (Jun. 2011)
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JCP 2011 Vol.6(6): 1168-1174 ISSN: 1796-203X
doi: 10.4304/jcp.6.6.1168-1174

Content Based Image Retrieval using the Generalized Gamma Density to model BEMD‟s IMFs

Aziza Benkuider, Aarab Abdellah
LESSI, Faculty of sciences Dhar el mahraz P.Box 1796 Department of physic, Fez, Morocco
Abstract—In this paper, we present a texture-image retrieval approach, which is based on the idea of to characterize images without extracting local features, by using global information extracted from the image Bidimensinal Empirical Mode Decomposition (BEMD) together with the Generalized Gamma (GG) Density. The BEMD method decompose image into a set of functions named Intrinsic Mode Function (IMF) and residue. The Generalized Gamma (GG) Density is used to represent the coefficients derived from each IMF and the Kullback- Leibler Distance (KLD) compute the similarity between Gamma Generalized function’s coefficients. The experimental results indicate that our approach can achieve higher retrieval rates.

Index Terms—Content Based Image Retrieval (CBIR), Empirical Mode Decomposition (EMD), Bidimensional Empirical Mode Decomposition (BEMD), Fast and Adaptive Bidimensional Empirical Mode Decomposition (FABEMD), Intrinsic Modes Functions (IMF), Generalized Gamma (GG), Kullback-Leibler (KL).

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Cite: Aziza Benkuider, Aarab Abdellah , "Content Based Image Retrieval using the Generalized Gamma Density to model BEMD‟s IMFs," Journal of Computers vol. 6, no. 6, pp. 1168-1174, 2011.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Monthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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