Wavelet-based texture retrieval modeling the magnitudes of wavelet detail coefficients with a generalized Gamma distribution
2010, 2010 20th International Conference on Pattern Recognition, 221-224, 2010Citas: 15
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Autor(es)
Esther de Ves and Xaro Benavent and Ana Ruedin and Daniel Acevedo and Leticia Seijas
Abstract
This paper presents a texture descriptor based on the fine detail coefficients at three resolution levels of a traslation invariant undecimated wavelet transform. First, we consider vertical and horizontal wavelet detail coefficients at the same position as the components of a bivariate random vector, and the magnitude and angle of these vectors are computed. The magnitudes are modeled by a Generalized Gamma distribution. Their parameters, together with the circular histograms of angles, are used to characterize each texture image of the database. The Kullback-Leibler divergence is used as the similarity measurement. Retrieval experiments, in which we compare two wavelet transforms, are carried out on the Brodatz texture collection. Results reveal the good performance of this wavelet-based texture descriptor obtained via the Generalized Gamma distribution.
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# | Año | #Citas | Título | Autores | Journal | Editor |
---|---|---|---|---|---|---|
1 | 2011 | 98 | Wavelet modeling using finite mixtures of generalized Gaussian distributions: Application to texture discrimination and retrieval | MS Allili | IEEE Transactions on Image Processing, 2011 | ieeexplore.ieee.org |
2 | 2015 | 55 | Numpy/scipy recipes for data science: k-medoids clustering | C Bauckhage | Researchgate. Net, February, 2015 | researchgate.net |
3 | 2014 | 35 | A statistical model for magnitudes and angles of wavelet frame coefficients and its application to texture retrieval | E de Ves, D Acevedo, A Ruedin, X Benavent | Pattern Recognition, 2014 | Elsevier |
4 | 1401 | 26 | Computing the kullback-leibler divergence between two generalized gamma distributions | C Bauckhage | arXiv preprint arXiv:1401.6853, 2014 | arxiv.org |
5 | 2019 | 5 | A finite mixture of weibull-based statistical model for texture retrieval in the complex wavelet domain | H Rami, AD El Maliani, M El Hassouni | IEEE Access, 2019 | ieeexplore.ieee.org |
6 | 2022 | 0 | A Review on Plant Diseases Detection | H Boukbir, AD El Maliani | 2022 9th International Conference …, 2022 | ieeexplore.ieee.org |
7 | 2015 | 3 | Model Based Approach for Content Based Image Retrievals Based on Fusion and Relevancy Methodology. | TV Madhusudhanarao, SP Setty… | … Arab Journal of …, 2015 | search.ebscohost.com |
8 | 2014 | 2 | Content based Image Retrievals for Brain Related Diseases | TVM Rao, SP Setty, Y Srinivas | International Journal of Computer …, 2014 | researchgate.net |
9 | 2021 | 0 | Multimodal and multivariate texture representation based on the finite mixtures of generalized gaussians with applications | ND Yapi | 2021 | di.uqo.ca |
10 | 2015 | 1 | Content-based image retrievals based on generalised gamma distribution and relevance feedback mechanism | TV Madhusudhanarao, SP Setty… | International Journal of …, 2015 | inderscienceonline.com |
11 | 2017 | 0 | Statistical inference for multi-state reliability systems | AS Makrides | 2017 | gnosis.library.ucy.ac.cy |
12 | 2019 | 1 | 杨鹏, 张凡龙, 杨章静 | 控制与决策, 2019 | ||
13 | 2017 | 0 | A medical image identification system based on mixture models | TVM Rao, Y Srinivas | 2017 International Conference on …, 2017 | ieeexplore.ieee.org |
14 | 0 | IOWA operators and its application to image retrieval | P Zuccarello, G Ayala, T Leon, E de Ves | researchgate.net | ||
15 | 2014 | 0 | IOWA Operators and Its Application to Image Retrieval | E de Ves, P Zuccarello, T Leon, G Ayala | Structural, Syntactic, and …, 2014 | Springer |