Introspective semantic segmentation

Traditional approaches for semantic segmentation work in a supervised setting assuming a fixed number of semantic categories and require sufficiently large training sets. The performance of various approaches is often reported in terms of average per pixel class accuracy and global accuracy of the final labeling. When applying the learned models in the practical settings on large amounts of unlabeled data, possibly containing previously unseen categories, it is important to properly quantify their performance by measuring a classifier's introspective capability. We quantify the confidence of the region classifiers in the context of a non-parametric k-nearest neighbor (k-NN) framework for semantic segmentation by using the so called strangeness measure. The proposed measure is evaluated by introducing confidence based image ranking and showing its feasibility on a dataset containing a large number of previously unseen categories.