Non-Subsampled Contourlet Texture Retrieval Using Four Estimators

Abstract:

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.

• Periodical: Applied Mechanics and Materials (Volumes 263-266)
• Pages: 167-170
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.263-266.167
• Online since: December 2012
• Authors: Xin Wu Chen, Jing Ge, Jin Gen Liu
• Keywords: Kurtosis, L1-Energy, L2-Energy, Non-Subsampled Contourlet Transform, Standard Deviation, Texture Image Retrieval

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