Abstract
In this paper, we propose a new algorithm for color texture retrieval by combining the feature extraction and similarity measurement tasks into a joint modeling and classification scheme based on the statistical representation of hypercomplex wavelets. The proposed statistical model leads to a new hypercomplex wavelet-based color texture retrieval method that is based on the accurate modeling of the marginal distribution of the hypecomplex wavelet coefficients using generalized Gaussian density (GGD) and on the existence a closed form for the Kullbak-Leibler distance (KLD) between GGDs. The efficiency of the proposed retrieval method is characterized by greater accuracy and flexibility in capturing the color texture information. Using a database of 640 color textures, the experimental results indicate that the proposed method significantly improves retrieval rates compared with traditional color approaches, while it enjoys a similar computational complexity.
Original language | English |
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Pages | 121-126 |
Number of pages | 6 |
DOIs | |
State | Published - 2009 |
Event | 2009 International Symposium on Bio-inspired, Learning, and Intelligent Systems for Security, BLISS 2009 - Edinburgh, United Kingdom Duration: 20 Aug 2009 → 22 Aug 2009 |
Conference
Conference | 2009 International Symposium on Bio-inspired, Learning, and Intelligent Systems for Security, BLISS 2009 |
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Country/Territory | United Kingdom |
City | Edinburgh |
Period | 20/08/09 → 22/08/09 |
ASJC Scopus subject areas
- Artificial Intelligence
- Information Systems
- Software