PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Efficient Parallel Feature Selection for Steganography Problems
Alberto Guillen, Antti Sorjamaa, Yoan Miche, Amaury Lendasse and Ignacio Rojas
In: Bio-Inspired Systems: Computational and Ambient Intelligence Lecture Notes in Computer Science , 5517/2009 . (2009) Springer Berlin / Heidelberg , pp. 1224-1231. ISBN 978-3-642-02477-1

Abstract

The steganography problem consists of the identification of images hiding a secret message, which cannot be seen by visual inspection. This problem is nowadays becoming more and more important since the World Wide Web contains a large amount of images, which may be carrying a secret message. Therefore, the task is to design a classifier, which is able to separate the genuine images from the non-genuine ones. However, the main obstacle is that there is a large number of variables extracted from each image and the high dimensionality makes the feature selection mandatory in order to design an accurate classifier. This paper presents a new efficient parallel feature selection algorithm based on the Forward-Backward Selection algorithm. The results will show how the parallel implementation allows to obtain better subsets of features that allow the classifiers to be more accurate.

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6658
Deposited By:Amaury Lendasse
Deposited On:08 March 2010