Abstract
Image segmentation benefits from using multi-feature ensembles. In this paper, we propose a novel multi-layer bipartite graph model for more effective feature fusion. This model employs multiple graph layers, each representing a feature space. They share common vertices but have individual edge sets that are obtained from different feature spaces. The features are fused by the regularization defined on a Grassmann manifold, which compresses the graph layers into subspace representations and merges them into one. The merged graph is then fed into a graph-cut algorithm to generate the final segmentation. Experiments carried out using the Berkeley segmentation benchmark show that our model is effective in feature fusion and image segmentation, outperforming the state-of-the-art superpixel-based methods. (C) 2019 Elsevier B.V. All rights reserved.