Paper Title:
Wavelet-Contourlet Retrieval Using Energy and Kurtosis Features
  Abstract

To improve the retrieval rate of contourlet transform retrieval system and reduce the redundancy of contourlet which cost two much time in building feature vector database, a new wavelet-contourlet transform retrieval system was proposed. Six different features, including mean, standard deviation, absolute mean energy, L2 energy, skewness and kurotis contributions to retrieval rates were examined. Based on the single feature ability in retrieval system, a new contourlet retrieval system was proposed. The feature vectors were constructed by cascading the 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 using the features cascaded by absolute mean and kurtosis can lead to a higher retrieval rate than several contourlet transform retrieval systems which utilize the combination feature of standard deviation and absolute mean energy most commonly used today under same dimension of feature vectors.

  Info
Periodical
Advanced Materials Research (Volumes 201-203)
Edited by
Daoguo Yang, Tianlong Gu, Huaiying Zhou, Jianmin Zeng and Zhengyi Jiang
Pages
2330-2333
DOI
10.4028/www.scientific.net/AMR.201-203.2330
Citation
X. W. Chen, Z. Q. Ma, L. W. Liu, "Wavelet-Contourlet Retrieval Using Energy and Kurtosis Features", Advanced Materials Research, Vols. 201-203, pp. 2330-2333, 2011
Online since
February 2011
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Price
$32.00
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