Non-Subsampled Contourlet Texture Retrieval Using Four Estimators

Contourlet transform is superior to wavelet transform in representing texture information and sparser in describing geometric structures in digital images, but lack of robust character of shift invariance. Non-subsampled contourlet transform (NSCT) alleviates this shortcoming hence more suitable for texture and has been studied for image de-noising, enhancement, and retrieval situations. Focus on improving the retrieval rates of existing contourlet transforms retrieval systems, a new texture retrieval algorithm was proposed. In the algorithm, texture information was represented by four statistical estimators, namely, L2-energy, kurtosis, standard deviation and L1-energy of each sub-band coefficients in NSCT domain. Experimental results show that the new algorithm can make a higher retrieval rate than the combination of standard deviation and energy which is most commonly used today.