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

Entropy

Abdourrahmane M. Atto¹  Yannick Berthoumieu² 

[1] 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

关键词: texture descriptors;    stochasticity measurements;    semantic gap;    parametric modeling;   

DOI  :  10.3390/e15114782

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.