Paper Title:
Material Texture Retrieval Using Contourlet-2.3 and Three Statistical Features
  Abstract

To improve the retrieval rate of contourlet transform texture retrieval system, a contourlet-2.3 transform based retrieval system was proposed. Six different features, including mean, standard deviation, absolute mean energy, L2 energy, skewness and kurtosis contributions to retrieval rates were examined. Based on the single feature ability in retrieval system, a contourlet-2.3 retrieval system was proposed. The feature vectors were constructed by cascading the standard deviation, absolute mean energy and kurtosis of each sub-band contourlet coefficients and the similarity measure used here is Canberra distance. Experimental results on 109 brodatz texture images show that the new retrieval algorithm can lead to a higher retrieval rate than several contourlet transform retrieval systems including the original contourlet transform, non-subsampled contourlet transform under the same structure.

  Info
Periodical
Advanced Materials Research (Volumes 233-235)
Edited by
Zhong Cao, Lixian Sun, Xueqiang Cao, Yinghe He
Pages
2495-2498
DOI
10.4028/www.scientific.net/AMR.233-235.2495
Citation
X. W. Chen, Z. Q. Ma, "Material Texture Retrieval Using Contourlet-2.3 and Three Statistical Features", Advanced Materials Research, Vols. 233-235, pp. 2495-2498, 2011
Online since
May 2011
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Price
$32.00
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