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SAC
2008
ACM

Towards automatic feature vector optimization for multimedia applications

9 years 28 days ago
Towards automatic feature vector optimization for multimedia applications
We systematically evaluate a recently proposed method for unsupervised discrimination power analysis for feature selection and optimization in multimedia applications. A series of experiments using real and synthetic benchmark data is conducted, the results of which indicate the suitability of the method for unsupervised feature selection and optimization. We present an approach for generating synthetic feature spaces of varying discrimination power, modeling main characteristics from real world feature vector extractors. A simple, yet powerful visualization is used to communicate the results of the automatic analysis to the user. Categories and Subject Descriptors H.2.4 [Information Systems]: Multimedia Databases; I.5.2 [Pattern Recognition]: Feature evaluation and selection Keywords Feature vectors, discrimination power, feature selection, Selforganizing map.
Tobias Schreck, Dieter W. Fellner, Daniel A. Keim
Added 28 Dec 2010
Updated 28 Dec 2010
Type Journal
Year 2008
Where SAC
Authors Tobias Schreck, Dieter W. Fellner, Daniel A. Keim
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