Online figure–ground segmentation with adaptive metrics in generalized LVQ

We address the problem of fast figure–ground segmentation of single objects from cluttered backgrounds to improve object learning and recognition. For the segmentation, we use an initial foreground hypothesis to train a classifier for figure and ground on topographically ordered feature maps with generalized learning vector quantization. We investigate the contribution of several adaptive metrics to enable generalization to the main object parts and derive a foreground classification, which yields an improved bottom-up hypothesis. We show that metrics adaptation is a powerful enrichment, where generalizing the Euclidean metrics towards local matrices of relevance factors leads to a higher classification accuracy and considerable robustness on partially inconsistent supervised information. Additionally, we verify our results in an online learning scenario and show that figure–ground segregation using this adaptive metrics enables a considerably higher recognition performance on segmented object views.