Iterative algorithm for interactive co-segmentation using semantic information propagation

This paper introduces a novel iterative approach for interactive single or multiple foreground co-segmentation using semantic information. A quadratic cost function based on a graph model is proposed. The cost function includes a 'smoothness' and a 'label-information' terms. The 'label-information' term propagates the feature-level and contextual information. This information is updated based on the features and neighborhood patterns of all the images after each iteration. The approach can be easily implemented with a few scribbles on a few random images. The paper also proposes a model called Neighborhood Pattern Model (NPM) for contextual information. Along with feature level information, NPM helps to give semantic meanings to the labels (i.e., foreground(s) and background). Moreover, in the case of insufficient features (i.e., same features for different labels), NPM can be effective to distinct the labels. Experimental results on two benchmark datasets, iCoseg and FlickrMFC, illustrate the better performance of the proposed approach over the current state-of-the-art co-segmentation methods.