# DAVIDSTUTZ

07thMARCH2018

• pixel-based affinity mapping: instead of predicting instance numbers (which are subject to permutations), $5 \times 5$ pixel patches are extracted and based on the instance labels a $25 \times 25$ affiny matrix is constructed. The affinity matrices over all these patches are clustered into $100$ classes using $k$-means. The network learns to predict these classes such that a global affinity map can be reconstructed by projecting the $5 \times 5$ affinity patches into the images (the affinity patches are reconstructed from the predicted cluster). Given the affinity matrix, a spectral clustering algorithm is used to segment the instances.