Incremental Figure-Ground Segmentation Using Localized Adaptive Metrics in LVQ

Abstract

Abstract. Vector quantization methods are confronted with a model selection problem, namely the number of prototypical feature representatives to model each class. In this paper we present an incremental learning scheme in the context of figure-ground segmentation. In presence of local adaptive metrics and supervised noisy information we use a parallel evaluation scheme combined with a local utility function to organize a learning vector quantization (LVQ) network with an adaptive number of prototypes and verify the capabilities on a real world figure-ground segmentation task.