Classifier dependent feature preprocessing methods

Benjamin M. Rodriguez; Gilbert L. Peterson

doi:10.1117/12.785472

18 April 2008 Classifier dependent feature preprocessing methods

Benjamin M. Rodriguez, Gilbert L. Peterson

Proceedings Volume 6982, Mobile Multimedia/Image Processing, Security, and Applications 2008; 69820S (2008) https://doi.org/10.1117/12.785472
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

Abstract

In mobile applications, computational complexity is an issue that limits sophisticated algorithms from being implemented on these devices. This paper provides an initial solution to applying pattern recognition systems on mobile devices by combining existing preprocessing algorithms for recognition. In pattern recognition systems, it is essential to properly apply feature preprocessing tools prior to training classification models in an attempt to reduce computational complexity and improve the overall classification accuracy. The feature preprocessing tools extended for the mobile environment are feature ranking, feature extraction, data preparation and outlier removal. Most desktop systems today are capable of processing a majority of the available classification algorithms without concern of processing while the same is not true on mobile platforms. As an application of pattern recognition for mobile devices, the recognition system targets the problem of steganalysis, determining if an image contains hidden information. The measure of performance shows that feature preprocessing increases the overall steganalysis classification accuracy by an average of 22%. The methods in this paper are tested on a workstation and a Nokia 6620 (Symbian operating system) camera phone with similar results.

Citation Download Citation

Benjamin M. Rodriguez and Gilbert L. Peterson "Classifier dependent feature preprocessing methods", Proc. SPIE 6982, Mobile Multimedia/Image Processing, Security, and Applications 2008, 69820S (18 April 2008); https://doi.org/10.1117/12.785472

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