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

Stochasticity: A Feature for the Structuring of Large and Heterogeneous Image Databases

1,* , 2 and 2
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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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 texture descriptors; stochasticity measurements; semantic gap; parametric modeling
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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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.

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