Stochasticity: A Feature for the Structuring of Large and Heterogeneous Image Databases
AbstractThe paper addresses image feature characterization and the structuring of large and heterogeneous image databases through the stochasticity or randomness appearance. Measuring stochasticity involves finding suitable representations that can significantly reduce statistical dependencies of any order. Wavelet packet representations provide such a framework for a large class of stochastic processes through an appropriate dictionary of parametric models. From this dictionary and the Kolmogorov stochasticity index, the paper proposes semantic stochasticity templates upon wavelet packet sub-bands in order to provide high level classification and content-based image retrieval. The approach is shown to be relevant for texture images.
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Atto, A.M.; Berthoumieu, Y.; Mégret, R. Stochasticity: A Feature for the Structuring of Large and Heterogeneous Image Databases. Entropy 2013, 15, 4782-4801.
Atto AM, Berthoumieu Y, Mégret R. Stochasticity: A Feature for the Structuring of Large and Heterogeneous Image Databases. Entropy. 2013; 15(11):4782-4801.Chicago/Turabian Style
Atto, Abdourrahmane M.; Berthoumieu, Yannick; Mégret, Rémi. 2013. "Stochasticity: A Feature for the Structuring of Large and Heterogeneous Image Databases." Entropy 15, no. 11: 4782-4801.