Affinity Derivation and Graph Merge for Instance Segmentation

Yiding Liu, Siyu Yang, Bin Li, Wengang Zhou, Jizheng Xu, Houqiang Li, Yan Lu

Abstract: We present an instance segmentation scheme based on pixel affinity information, which is the relationship of two pixels belonging to the same instance. In our scheme, we use two neural networks with similar structures. One predicts the pixel level semantic score and the other is designed to derive pixel affinities. Regarding pixels as the vertexes and affinities as edges, we then propose a simple yet effective graph merge algorithm to cluster pixels into instances. Experiments show that our scheme generates fine grained instance masks. With Cityscape training data, the proposed scheme achieves 27.3 AP on test set.