  This paper presents a nonparametric scene parsing approach that improves the
overall accuracy, as well as the coverage of foreground classes in scene
images. We first improve the label likelihood estimates at superpixels by
merging likelihood scores from different probabilistic classifiers. This boosts
the classification performance and enriches the representation of
less-represented classes. Our second contribution consists of incorporating
semantic context in the parsing process through global label costs. Our method
does not rely on image retrieval sets but rather assigns a global likelihood
estimate to each label, which is plugged into the overall energy function. We
evaluate our system on two large-scale datasets, SIFTflow and LMSun. We achieve
state-of-the-art performance on the SIFTflow dataset and near-record results on
LMSun.
