Adaptive image retrieval based on generalized Gaussian model and LBP

Author

Yang, Xiaohui ; Yao, Xueyan ; Li, Dengfeng ; Cai, Lijun

Author_Institution

Inst. of Appl. Math., Henan Univ., Kaifeng, China

fYear

2010

fDate

16-17 Aug. 2010

Firstpage

103

Lastpage

107

Abstract

This paper proposes an adaptive image retrieval method via spatial-frequency mixed features (SFMF). The SFMF can describe spatial and frequency information of image simultaneously. More specifically, spatial feature is local binary pattern (LBP) histogram extracted from image. Frequency features are described as the generalized Gaussian density (GGD) of Contourlet transform detail coefficients and LBP histogram of approximation coefficients. Further, we use closed-loop feedback to adjust weighting factor adoptively for image retrieval. Experiments show that average recall rate of this method is 12.08%, 10.23% higher than frequency domain method and LBP respectively.

Keywords

Gaussian processes; content-based retrieval; feature extraction; image retrieval; adaptive image retrieval method; closed-loop feedback; contourlet transform detail coefficients; generalized Gaussian density; generalized Gaussian model; local binary pattern histogram; spatial-frequency mixed features; Adaptation model; Feature extraction; Histograms; Image retrieval; Pixel; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Web Society (SWS), 2010 IEEE 2nd Symposium on

Conference_Location

Beijing

Print_ISBN

978-1-4244-6356-5

Type

conf

DOI

10.1109/SWS.2010.5607469

Filename

5607469