- Title
- Comparison of curvelet and wavelet texture features for content based image retrieval
- Creator
- Sumana, Ishrat; Lu, Guojun; Zhang, Dengsheng
- Date
- 2012
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/161577
- Identifier
- vital:12495
- Identifier
-
https://doi.org/10.1109/ICME.2012.90
- Abstract
- Texture feature plays a vital role in content based Image retrieval (CBIR). Wavelet texture feature modeled by generalized Gaussian density (GGD) [1] performs better than discrete wavelet texture feature. Curve let texture feature was proposed in [2]. In this paper, we compute a new texture feature by applying the generalized Gaussian density to the distribution of curve let coefficients which we call curve let GGD texture feature. The purpose of this paper is to investigate curve let GGD texture feature and compare its retrieval performance with that of curve let, wavelet and wavelet GGD texture features. Experimental results show that both curve let and curve let GGD features perform significantly better than wavelet and wavelet GGD texture features. Among the two types of curve let based features, curve let feature shows better performance in CBIR than curve let GGD texture feature. The findings are discussed in the paper.
- Publisher
- IEEE
- Relation
- 2012 IEEE International Conference on Multimedia and Expo (ICME) p. 290-295
- Rights
- © 2012 IEEE
- Rights
- This metadata is freely available under a CCO license
- Subject
- S-curvelet transform; Wavelet transform; Curvelet GGD texture feature; ML estimator; Content based image retrieval
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