Article ID Journal Published Year Pages File Type
409100 Neurocomputing 2008 12 Pages PDF
Abstract

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.

Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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