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Entropy 2013, 15(11), 4782-4801; doi:10.3390/e15114782

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 / Revised: 27 September 2013 / Accepted: 29 October 2013 / Published: 4 November 2013
(This article belongs to the Special Issue Big Data)
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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. View Full-Text
Keywords: texture descriptors; stochasticity measurements; semantic gap; parametric modeling texture descriptors; stochasticity measurements; semantic gap; parametric modeling

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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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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.

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