Scene parsing by nonparametric label transfer of content-adaptive windows

• CollageParsing is a scene parsing algorithm that matches content-adaptive windows.
• Unlike superpixels, content-adaptive windows are designed to preserve objects.
• A powerful MRF unary is constructed by performing label transfer using the windows.
• Gains of 15–19% average per-class accuracy are obtained on a standard benchmark.

Scene parsing is the task of labeling every pixel in an image with its semantic category. We present CollageParsing, a nonparametric scene parsing algorithm that performs label transfer by matching content-adaptive windows. Content-adaptive windows provide a higher level of perceptual organization than superpixels, and unlike superpixels are designed to preserve entire objects instead of fragmenting them. Performing label transfer using content-adaptive windows enables the construction of a more effective Markov random field unary potential than previous approaches. On a standard benchmark consisting of outdoor scenes from the LabelMe database, CollageParsing obtains state-of-the-art performance with 15–19% higher average per-class accuracy than recent nonparametric scene parsing algorithms.