Texture Image Retrieval Based on Nonsubsampled Shearlet Transform and Rotation Invariant Local Phase Quantization
Min Yin · Jisuanji gongcheng · 2014
For a single method of extracting image feature retrieval defective,this paper proposes a combination of Nonsubsampled Shearlet Transform(NSST)statistical features and Rotation Invariant Local Phase Quantization(RILPQ)texture image retrieval method. NSST exhibits highly directional sensitivity and shift invariance,even it can be sparse representation of the image. In contrast,NSST acquires the natural texture and edge information with the traditional wavelet and has higher computational efficiently with nonsubsampled contourlet transform. This paper acquires the statistical features of the image NSST coefficients by Generalized Gaussian Distribution(GGD)function. Image features are directly extracted by RI-LPQ description operator. Texture images on the Brodatz image database are retrieved by the formula of similarity measure with weight coefficients. Experimental results show that average retrieval rate of NSST statistical features method is 4.77% and 1.44% higher than traditional wavelet method and Contourlet method respectively. The average retrieval rate of this paper method based on fused features is 7.36% and 1.98% higher than NSST method and RI-LPQ method respectively.