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.

• Type: Journal
• Year: 2008
• Where: SAC
• Authors: Tobias Schreck, Dieter W. Fellner, Daniel A. Keim
• Keywords: Applied Computing, Discrimination Power, Feature Selection, SAC 2008, Unsupervised Feature Selection