Robust object segmentation by adaptive metrics in Generalized LVQ
نویسندگان
چکیده
We investigate the effect of several adaptive metrics in the context of figure-ground segregation, using Generalized LVQ to train a classifier for image regions. Extending the Euclidean metrics towards local matrices of relevance-factors does not only lead to a higher classification accuracy and increased robustness on heterogeneous/noisy data, but also figureground segregation using this adaptive metrics enables a considerably higher recognition performance on segmented objects of real image data.
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