Incremental GRLVQ: Learning relevant features for 3D object recognition

Autor(en): Kietzmann, Tim C.
Lange, Sascha
Riedmiller, Martin
Stichwörter: adaptive metric; Computer Science; Computer Science, Artificial Intelligence; feature selection; incremental learning vector quantization; NETWORK; object recognition; relevance learning
Erscheinungsdatum: 2008
Herausgeber: ELSEVIER
Journal: NEUROCOMPUTING
Volumen: 71
Ausgabe: 13-15
Startseite: 2868
Seitenende: 2879
Zusammenfassung: 
We present a new variant of generalized relevance learning vector quantization (GRLVQ) in a computer vision scenario. A version with incrementally added prototypes is used for the non-trivial case of high-dimensional object recognition. Training is based upon a generic set of standard visual features, the learned input weights are used for iterative feature pruning. Thus, prototypes and input space are altered simultaneously, leading to very sparse and task-specific representations. The effectiveness of the approach and the combination of the incremental variant together with pruning was tested on the COIL100 database, It exhibits excellent performance with regard to codebook size, feature selection and recognition accuracy. (C) 2007 Elsevier B.V. All rights reserved.
ISSN: 09252312
DOI: 10.1016/j.neucom.2007.08.018

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