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Pré-Publication, Document De Travail Année : 2010

Stochasticity: A Feature for Analyzing and Understanding Textures, with Applications to Classification and Content-Based Image Retrieval

Résumé

Stochasticity is proposed as a feature for texture characterization and analysis. Measuring stochasticity requires 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. The paper first addresses the selection of the best wavelet packet basis with respect to the stochasticity criterion and by using the Kolmogorov stochasticity parameter. A best basis under stochasticity consideration makes possible accurate texture description trough a dictionary of parametric models, especially for non regular textures. Among the properties of such a representation, the paper shows that texture classification is possible through stochasticity consideration. The relevance of the analysis also makes possible content-based stochasticity retrieval with semantics and with respect to the order structure of the wavelet packet bases.
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Dates et versions

hal-00550318 , version 1 (26-12-2010)
hal-00550318 , version 2 (17-08-2013)
hal-00550318 , version 3 (04-11-2013)

Identifiants

  • HAL Id : hal-00550318 , version 1

Citer

Abdourrahmane Atto, Yannick Berthoumieu, Rémi Mégret. Stochasticity: A Feature for Analyzing and Understanding Textures, with Applications to Classification and Content-Based Image Retrieval. 2010. ⟨hal-00550318v1⟩
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