Material Texture Contourlet Retrieval by Energy and Variance Distribution Features

Abstract:

contourlet transform can extract image texture information more efficiently than wavelet transform and has been studied for image de-noising, enhancement, and retrieval situations, its low retrieval rate are still not satisfied due to feature extraction and other reasons. Focus on improving the retrieval rate of contourlet transform retrieval system, a new feature named variance distribution was proposed and a contourlet retrieval system was constructed in this paper. The feature vectors were constructed by cascading the energy and variance distribution of each sub-band coefficients and the similarity measure used here was Canberra distance. Experimental results show that using the new features can make a higher retrieval rate than the combination of standard deviation and energy which is most commonly used today under the same retrieval time and hardware complexity.

• Periodical: Advanced Materials Research (Volumes 562-564)
• Pages: 208-211
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.562-564.208
• Online since: August 2012
• Authors: Xin Wu Chen, Jing Ge, Li Ping Che
• Keywords: Content Based Image Retrieval, Contourlet Transform, Energy, Texture Image Retrieval, Variance Distribution

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