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Stochasticity: A Feature for the Structuring of Large and Heterogeneous Image Databases
LISTIC (Laboratory of Informatics, Systems, Information and Knowledge Processing), University of Savoie, B.P. 80439, 74944 Annecy le Vieux Cedex, France
IMS (Laboratory of Material to System Integration), CNRS UMR 5218, University of Bordeaux, IPB, ENSEIRB-MATMECA, 351 cours de la libération, 33400 Talence, France
* Author to whom correspondence should be addressed.
Received: 26 August 2013; in revised form: 27 September 2013 / Accepted: 29 October 2013 / Published: 4 November 2013
(This article belongs to the Special Issue Big Data
Abstract: The 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.
Keywords: texture descriptors; stochasticity measurements; semantic gap; parametric modeling
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MDPI and ACS Style
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.
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.