A new image retrieval algorithm based on the improved double density contourlet transform(DDCT) is proposed in this paper. The improved double density contourlet transform have the property of shift invariance and non-Gaussian for the high frequency sub-bands. In order to obtain the high frequency texture feature, we design the quantization histograms using the high frequency direction sub-bands. The 0/1 quantization is used to deal with the energy matrix of high frequency sub-bands. A texture point is defined as an 8-D vector by integrating different channel values from high frequency sub-bands and the quantization histograms are computed. In order to get the texture-spatial features, the local binary pattern is used to describe the texture feature of low frequency sub-band. Then the quantization histograms and the local binary pattern (LBP) can be used to denote the texture features of the image. Experiments show that the proposed algorithm using the improved double density contourlet transform outperforms the SD algorithm based on the contourlet transform in the natural image retrieval.
Digital Object Identifier (DOI)
Qingsong, Xie; Jie, Guo; Mengjun, Li; and Zhiyong, An
"Image Retrieval using the Improved Double Density Contourlet Transform,"
Applied Mathematics & Information Sciences: Vol. 09
, Article 47.
Available at: https://dc.naturalspublishing.com/amis/vol09/iss3/47